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Chen M, Li Y, Zhang K, Liu H. Protein coding regions prediction by fusing DNA shape features. N Biotechnol 2024; 80:21-26. [PMID: 38182076 DOI: 10.1016/j.nbt.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 11/14/2023] [Accepted: 12/23/2023] [Indexed: 01/07/2024]
Abstract
Exons crucial for coding are often hidden within introns, and the two tend to vary greatly in length, which results in deep learning-based protein coding region prediction methods often performing poorly when applied to more structurally complex biological genomes. DNA shape information also plays a role in revealing the underlying logic of gene expression, yet current methods ignore the influence of DNA shape features when distinguishing coding and non-coding regions. We propose a method to predict protein-coding regions using the CNNS-BRNN model, which incorporates DNA shape features and improves the model's ability to distinguish between intronic and exonic features. We use a fusion coding technique that combines DNA shape features and traditional sequence features. Experiments show that this method outperforms the baseline method in metrics such as AUC and F1 by 2.3% and 5.3%, respectively, and the fusion coding method that introduces DNA shape features has a significant improvement in model performance.
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Affiliation(s)
- Miao Chen
- Ocean University of China, College of Computer Science and Technology, Qingdao 266100, China
| | - Yangyang Li
- Ocean University of China, College of Computer Science and Technology, Qingdao 266100, China
| | - Kun Zhang
- Ocean University of China, College of Computer Science and Technology, Qingdao 266100, China
| | - Hao Liu
- Ocean University of China, College of Computer Science and Technology, Qingdao 266100, China.
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2
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Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS. Artificial neural network and convolutional neural network for prediction of dental caries. Spectrochim Acta A Mol Biomol Spectrosc 2024; 312:124063. [PMID: 38394882 DOI: 10.1016/j.saa.2024.124063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/12/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
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Affiliation(s)
- Katrul Nadia Basri
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia; Photonics Technology Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
| | - Farinawati Yazid
- Faculty of Dentistry, Universiti Kebangsaan Malaysia, 50300 Kuala Lumpur, Malaysia
| | | | - Zalhan Md Yusof
- Photonics Technology Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
| | - Rozina Abdul Rani
- School of Mechanical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - Ahmad Sabirin Zoolfakar
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
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3
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Koshelev DS. Expert System for Fourier Transform Infrared Spectra Recognition Based on a Convolutional Neural Network With Multiclass Classification. Appl Spectrosc 2024; 78:387-397. [PMID: 38281905 DOI: 10.1177/00037028241226732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Fourier transform infrared spectroscopy (FT-IR) is a widely used spectroscopic method for routine analysis of substances and compounds. Spectral interpretation of spectra is a labor-intensive process that provides important information about functional groups or bonds present in compounds and complex substances. In this paper, based on deep learning methods of convolutional neural networks, models were developed to determine the presence of 17 classes of functional groups or 72 classes of coupling oscillations in the FT-IR spectra. Using web scanning, the spectra of 14 361 FT-IR spectra of organic molecules were obtained. Several different variants of model architectures with different sizes of feature maps have been tested. Based on the Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GradCAM) methods, visualization tools have been developed for visualizing and highlighting the areas of absorption bands manifestation for corresponding functional groups or bonds in the spectrum. To determine 17 and 72 classes, the F1-weighted metric, which is the harmonic mean of the class' precision and class' recall weighted by class' fraction, reached 93 and 88%, respectively, when using data on the position of absorption maxima in the spectrum as an additional source layer. The resulting model can be used to facilitate the routine analysis of spectra for all areas such as organic chemistry, materials science, and biology, as well as to facilitate the preparation of the obtained experimental data for publication.
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Affiliation(s)
- Daniil S Koshelev
- Faculty of the Material Science, Lomonosov Moscow State University, Moscow, Russian Federation
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russian Federation
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Tewes TJ, Kerst M, Pavlov S, Huth MA, Hansen U, Bockmühl DP. Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms. Heliyon 2024; 10:e27824. [PMID: 38510034 PMCID: PMC10950671 DOI: 10.1016/j.heliyon.2024.e27824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/29/2024] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.
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Affiliation(s)
- Thomas J. Tewes
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Mario Kerst
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Svyatoslav Pavlov
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Miriam A. Huth
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
| | - Ute Hansen
- Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Friedrich-Heinrich-Allee, 47475, Kamp-Lintfort, Germany
| | - Dirk P. Bockmühl
- Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany
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Hu Y, Liu C, Wollheim WM. Prediction of riverine daily minimum dissolved oxygen concentrations using hybrid deep learning and routine hydrometeorological data. Sci Total Environ 2024; 918:170383. [PMID: 38280612 DOI: 10.1016/j.scitotenv.2024.170383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/12/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
Dissolved oxygen (DO) depletion is a severe threat to aquatic ecosystems. Hence, using easily available routine hydrometeorological variables without DO as inputs to predict the daily minimum DO concentration in rivers has huge practical significance in the watershed management. The daily minimum DO concentrations at the outlet of the Oyster River watershed in New Hampshire, USA, were predicted by a set of deep learning neural networks using meteorological data and high-frequency water level, water temperature, and specific conductance (CTD) data measured within the watershed. The dependent variable, DO concentration, was measured at the outlet. From April 2013 to March 2018, the dataset was separated into training, validation, and test portions with a ratio of 5:3:3. A Long Short-Term Memory (LSTM) model and a hybrid Convolutional Neural Networks (CNN-LSTM) model were trained and evaluated for predicting the daily minimum DO concentration. The hybrid CNN-LSTM model exhibited the better predictive stability but the comparable accuracy (the mean R2 value = 0.865) compared with the pure LSTM model (the mean R2 value = 0.839). The model performance (both the stability and accuracy) was improved by aggregating the input data frequency from 15 min of raw data to 24 h. Likewise, the modeling performance didn't benefit from including 'forecasted' meteorological data at the predicted time step in the input dataset. This study provided an efficient and low-cost approach to predict the water quality in rivers and streams to realize the scientific watershed management.
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Affiliation(s)
- Yue Hu
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China
| | - Chuankun Liu
- Sichuan Academy of Environmental Policy and Planning, Department of Ecology and Environment of Sichuan Province, Chengdu 610059, China.
| | - Wilfred M Wollheim
- Department of Natural Resources and Environment, University of New Hampshire, Durham, NH 03824, USA
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V S, Ramasubba Reddy M. Classification of Motor Imagery EEG signals using high resolution Time-Frequency Representations and Convolutional Neural network. Biomed Phys Eng Express 2024. [PMID: 38513274 DOI: 10.1088/2057-1976/ad3647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.
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Affiliation(s)
- Srimadumathi V
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, 600036, Chennai, 600036, INDIA
| | - MachiReddy Ramasubba Reddy
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai-600036, Chennai, Tamil Nadu, 600036, INDIA
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Jin Z, Zhou Q, Pei Z, Chen G. Research on spatial localization method of composite damage under strong noise. Ultrasonics 2024; 140:107301. [PMID: 38522167 DOI: 10.1016/j.ultras.2024.107301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/25/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
In this paper, a damage spatial imaging approach based on novel signal extraction is suggested to reconstruct the Lamb wave response signal under strong noise and realize the spatial localization of damage. First, the Variable Mode Decomposition (VMD) parameters are optimized by the improved Grey Wolf optimization method (IGWO) to decompose the response signal. To rebuild the response signal, the correlation coefficient is used to choose the optimal modal component and the residual. To give the best wavelet function and transform level for adaptive denoising of the reconstructed signal without a reference signal, an enhanced Discrete Wavelet Transform (DWT) based on Shannon entropy is proposed. To achieve damage localization imaging, a damage spatial localization model is built utilizing a reconstruction algorithm for probabilistic inspection of damage (RAPID) approach and a convolutional neural network (CNN). The suggested method may successfully increase the signal-to-noise ratio (SNR) of the reconstructed response signal and lower the error of spatial localization under strong noise through experiments. The spatial localization of composite damage using Lamb wave under strong noise is expanded in this paper.
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Affiliation(s)
- Zhongyan Jin
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Qihong Zhou
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China.
| | - Zeguang Pei
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
| | - Ge Chen
- College of Mechanical Engineering, Donghua University, Shanghai 201620, China
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Zhou T, Wang H, Du Y, Liu F, Guo Y, Lu H. M 3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection. Comput Biol Med 2024; 173:108291. [PMID: 38522254 DOI: 10.1016/j.compbiomed.2024.108291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. METHODS To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. RESULTS The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. CONCLUSION The M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.
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Affiliation(s)
- Tao Zhou
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Hongwei Wang
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China.
| | - Yuhu Du
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Fengzhen Liu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Yujie Guo
- School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China
| | - Huiling Lu
- School of Medical Information and Engineering, Ningxia Medical University, Yinchuan, 750004, China.
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Nakao Y, Nishihara T, Sasaki R, Fukushima M, Miuma S, Miyaaki H, Akazawa Y, Nakao K. Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma. Sci Rep 2024; 14:6576. [PMID: 38503827 PMCID: PMC10951210 DOI: 10.1038/s41598-024-57078-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predict treatment efficacy using pre-ICIs contrast-enhanced computed tomography (CT) imaging characteristics. We evaluated the efficacy of atezolizumab and bevacizumab in 43 patients at the Nagasaki University Hospital from 2020 to 2022 using the modified Response Evaluation Criteria in Solid Tumors. A total of 197 Progressive Disease (PD), 271 Partial Response (PR), and 342 Stable Disease (SD) contrast CT images of HCC were used for training. We used ResNet-18 as the Convolutional Neural Network (CNN) model and YOLOv5, YOLOv7, YOLOv8 as the You Only Look Once (YOLO) model with precision-recall curves and class activation maps (CAMs) for diagnostic performance evaluation and model interpretation, respectively. The 3D t-distributed Stochastic Neighbor Embedding was used for image feature analysis. The YOLOv7 model demonstrated Precision 53.7%, Recall 100%, F1 score 69.8%, mAP@0.5 99.5% for PD, providing accurate and clinically versatile predictions by identifying decisive points. The ResNet-18 model had Precision 100% and Recall 100% for PD. However, the CAMs sites did not align with the tumors, suggesting the CNN model is not predicting that a given CT slice is PD, PR, or SD, but that it accurately predicts Individual Patient's CT slices. Preparing substantial training data for tumor drug effect prediction models is challenging compared to general tumor diagnosis models; hence, large-scale validation using an efficient YOLO model is warranted.
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Affiliation(s)
- Yasuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan.
| | - Takahito Nishihara
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Gastroenterology and Hepatology, Isahaya General Hospital, 24-1 Eishohigashimachi, Isahaya, Nagasaki, Japan
| | - Ryu Sasaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Masanori Fukushima
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Satoshi Miuma
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Hisamitsu Miyaaki
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Yuko Akazawa
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
- Department of Histology and Cell Biology, Nagasaki University Graduate School of Biomedical Sciences, 1-12-4 Sakamoto, Nagasaki City, Nagasaki, Japan
| | - Kazuhiko Nakao
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki City, Nagasaki, Japan
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Ispizua Yamati FR, Günder M, Barreto A, Bömer J, Laufer D, Bauckhage C, Mahlein AK. Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning. Plant Dis 2024:PDIS04230779RE. [PMID: 37755420 DOI: 10.1094/pdis-04-23-0779-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.
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Affiliation(s)
| | - Maurice Günder
- Institute for Computer Science III, University of Bonn, DE - 53115 Bonn, Germany
| | - Abel Barreto
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Jonas Bömer
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Daniel Laufer
- Institute of Sugar Beet Research (IfZ), DE - 37079 Göttingen, Germany
| | - Christian Bauckhage
- Institute for Computer Science III, University of Bonn, DE - 53115 Bonn, Germany
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Zhang S, Jing Y, Liang Y. EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides. Curr Med Chem 2024; 31:CMC-EPUB-139203. [PMID: 38494930 DOI: 10.2174/0109298673287899240303164403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/13/2024] [Accepted: 02/19/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-Cov) and SARS-COV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study. METHODS Given this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset. RESULTS Experimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides. CONCLUSION The results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.- com/JYY625/EACVP.git.
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Affiliation(s)
- Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, 571158, P. R. China
| | - Yuanyuan Jing
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China
| | - Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an 710048, P. R. China
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12
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Fraehr N, Wang QJ, Wu W, Nathan R. Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models. Water Res 2024; 252:121202. [PMID: 38290237 DOI: 10.1016/j.watres.2024.121202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/21/2024] [Accepted: 01/23/2024] [Indexed: 02/01/2024]
Abstract
Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.
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Affiliation(s)
- Niels Fraehr
- Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria 3010, Australia.
| | - Quan J Wang
- Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria 3010, Australia
| | - Wenyan Wu
- Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria 3010, Australia
| | - Rory Nathan
- Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Victoria 3010, Australia
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Wu T, Xia Z, Zhou M, Kong LB, Chen Z. AMENet is a monocular depth estimation network designed for automatic stereoscopic display. Sci Rep 2024; 14:5868. [PMID: 38467677 PMCID: PMC10928105 DOI: 10.1038/s41598-024-56095-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 03/01/2024] [Indexed: 03/13/2024] Open
Abstract
Monocular depth estimation has a wide range of applications in the field of autostereoscopic displays, while accuracy and robustness in complex scenes are still a challenge. In this paper, we propose a depth estimation network for autostereoscopic displays, which aims at improving the accuracy of monocular depth estimation by fusing Vision Transformer (ViT) and Convolutional Neural Network (CNN). Our approach feeds the input image as a sequence of visual features into the ViT module and utilizes its global perception capability to extract high-level semantic features of the image. The relationship between the losses is quantified by adding a weight correction module to improve robustness of the model. Experimental evaluation results on several public datasets show that AMENet exhibits higher accuracy and robustness than existing methods in different scenarios and complex conditions. In addition, a detailed experimental analysis was conducted to verify the effectiveness and stability of our method. The accuracy improvement on the KITTI dataset compared to the baseline method is 4.4%. In summary, AMENet is a promising depth estimation method with sufficient high robustness and accuracy for monocular depth estimation tasks.
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Affiliation(s)
- Tianzhao Wu
- College of New Materials and New Energies, Shenzhen University of Technology, Shenzhen, 518118, Guangdong, China
- College of Applied Technology, Shenzhen University, Shenzhen, 518060, Guangdong, China
| | - Zhongyi Xia
- College of New Materials and New Energies, Shenzhen University of Technology, Shenzhen, 518118, Guangdong, China
- College of Applied Technology, Shenzhen University, Shenzhen, 518060, Guangdong, China
| | - Man Zhou
- College of New Materials and New Energies, Shenzhen University of Technology, Shenzhen, 518118, Guangdong, China
- College of Applied Technology, Shenzhen University, Shenzhen, 518060, Guangdong, China
| | - Ling Bing Kong
- College of New Materials and New Energies, Shenzhen University of Technology, Shenzhen, 518118, Guangdong, China
| | - Zengyuan Chen
- College of New Materials and New Energies, Shenzhen University of Technology, Shenzhen, 518118, Guangdong, China.
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14
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Huang J, Ferreira PF, Wang L, Wu Y, Aviles-Rivero AI, Schönlieb CB, Scott AD, Khalique Z, Dwornik M, Rajakulasingam R, De Silva R, Pennell DJ, Nielles-Vallespin S, Yang G. Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study. Sci Rep 2024; 14:5658. [PMID: 38454072 PMCID: PMC10920645 DOI: 10.1038/s41598-024-55880-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 02/27/2024] [Indexed: 03/09/2024] Open
Abstract
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.
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Grants
- Wellcome Trust
- RG/19/1/34160 British Heart Foundation
- This study was supported in part by the UKRI Future Leaders Fellowship (MR/V023799/1), BHF (RG/19/1/34160), the ERC IMI (101005122), the H2020 (952172), the MRC (MC/PC/21013), the Royal Society (IEC/NSFC/211235), the NVIDIA Academic Hardware Grant Program, EPSRC (EP/V029428/1, EP/S026045/1, EP/T003553/1, EP/N014588/1, EP/T017961/1), and the Cambridge Mathematics of Information in Healthcare Hub (CMIH) Partnership Fund.
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Affiliation(s)
- Jiahao Huang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
| | - Pedro F Ferreira
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Lichao Wang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Department of Computing, Imperial College London, London, UK
| | - Yinzhe Wu
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Angelica I Aviles-Rivero
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Andrew D Scott
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Zohya Khalique
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Maria Dwornik
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ramyah Rajakulasingam
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Ranil De Silva
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Dudley J Pennell
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Sonia Nielles-Vallespin
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK.
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW7 2AZ, UK.
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK.
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15
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Zhao H, Liu Y, Li G, Lei D, Du Y, Li Y, Tang H, Dou X. Electrophilicity Modulation for Sub-ppm Visualization and Discrimination of EDA. Adv Sci (Weinh) 2024:e2400361. [PMID: 38447144 DOI: 10.1002/advs.202400361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/07/2024] [Indexed: 03/08/2024]
Abstract
Precise and timely recognition of hazardous chemical substances is of great significance for safeguarding human health, ecological environment, public security, etc., especially crucial for adopting appropriate disposition measures. Up to now, there remains a practical challenge to sensitively detect and differentiate organic amines with similar chemical structures with intuitive analysis outcomes. Here, a unique optical probe with two electrophilic recognition sites for rapid and ultra-sensitive ratiometric fluorescence detection of ethylenediamine (EDA) is presented, while producing distinct fluorescence signals to its structural analog. The probe exhibits ppb/nmol level sensitivity to liquidous and gaseous EDA, specific recognition toward EDA without disturbance to up to 28 potential interferents, as well as rapid fluorescence response within 0.2 s. By further combining the portable sensing chip with the convolutional algorithm endowed with image processing, this work cracked the problem of precisely discriminating the target and non-targets at extremely low concentrations.
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Affiliation(s)
- Hao Zhao
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, 832000, China
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yuan Liu
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Gaosheng Li
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Da Lei
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yuwan Du
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Yudong Li
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Hui Tang
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, 832000, China
| | - Xincun Dou
- Xinjiang Key Laboratory of Trace Chemical Substances Sensing, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
- Key Laboratory of Improvised Explosive Chemicals for State Market Regulation, Urumqi, 830011, China
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16
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Awais M, Abdal MN, Akram T, Alasiry A, Marzougui M, Masood A. An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization. Front Oncol 2024; 14:1328200. [PMID: 38505591 PMCID: PMC10949894 DOI: 10.3389/fonc.2024.1328200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/25/2024] [Indexed: 03/21/2024] Open
Abstract
In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.
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Affiliation(s)
- Muhammad Awais
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
- Department of Computer Engineering, TED University, Ankara, Türkiye
| | - Md. Nazmul Abdal
- Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh
| | - Tallha Akram
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah, Pakistan
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha, Saudi Arabia
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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17
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Kang L, Tang B, Huang J, Li J. 3D-MRI super-resolution reconstruction using multi-modality based on multi-resolution CNN. Comput Methods Programs Biomed 2024; 248:108110. [PMID: 38452685 DOI: 10.1016/j.cmpb.2024.108110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 01/28/2024] [Accepted: 03/01/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND OBJECTIVE High-resolution (HR) MR images provide rich structural detail to assist physicians in clinical diagnosis and treatment plan. However, it is arduous to acquire HR MRI due to equipment limitations, scanning time or patient comfort. Instead, HR MRI could be obtained through a number of computer assisted post-processing methods that have proven to be effective and reliable. This paper aims to develop a convolutional neural network (CNN) based super-resolution reconstruction framework for low-resolution (LR) T2w images. METHOD In this paper, we propose a novel multi-modal HR MRI generation framework based on deep learning techniques. Specifically, we construct a CNN based on multi-resolution analysis to learn an end-to-end mapping between LR T2w and HR T2w, where HR T1w is fed into the network to offer detailed a priori information to help generate HR T2w. Furthermore, a low-frequency filtering module is introduced to filter out the interference from HR-T1w during high-frequency information extraction. Based on the idea of multi-resolution analysis, detailed features extracted from HR T1w and LR T2w are fused at two scales in the network and then HR T2w is reconstructed by upsampling and dense connectivity module. RESULTS Extensive quantitative and qualitative evaluations demonstrate that the proposed method enhances the recovered HR T2w details and outperforms other state-of-the-art methods. In addition, the experimental results also suggest that our network has a lightweight structure and favorable generalization performance. CONCLUSION The results show that the proposed method is capable of reconstructing HR T2w with higher accuracy. Meanwhile, the super-resolution reconstruction results on other dataset illustrate the excellent generalization ability of the method.
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Affiliation(s)
- Li Kang
- College of Electronics and Information Engineering, Shenzhen University, the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China
| | - Bin Tang
- College of Electronics and Information Engineering, Shenzhen University, the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China
| | - Jianjun Huang
- College of Electronics and Information Engineering, Shenzhen University, the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China.
| | - Jianping Li
- College of Electronics and Information Engineering, Shenzhen University, the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China
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18
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Yao W, Bai J, Liao W, Chen Y, Liu M, Xie Y. From CNN to Transformer: A Review of Medical Image Segmentation Models. J Imaging Inform Med 2024:10.1007/s10278-024-00981-7. [PMID: 38438696 DOI: 10.1007/s10278-024-00981-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 03/06/2024]
Abstract
Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Moreover, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. Recently, the Segment Anything Model (SAM) and its variants have also been attempted for medical image segmentation. In this paper, we conduct a survey of the most representative seven medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on Tuberculosis Chest X-rays, Ovarian Tumors, and Liver Segmentation datasets. Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.
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Affiliation(s)
- Wenjian Yao
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Jiajun Bai
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Wei Liao
- Department of Obstetrics and Gynaecology, Deyang People's Hospital, 618000, Deyang, China
| | - Yuheng Chen
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Mengjuan Liu
- Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, 610054, Chengdu, China.
| | - Yao Xie
- Department of Obstetrics and Gynaecology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, 610072, Chengdu, China.
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19
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Xu P, Zhao J, Wan M, Song Q, Su Q, Wang D. Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks. Med Phys 2024. [PMID: 38436433 DOI: 10.1002/mp.16946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/03/2024] [Accepted: 01/09/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and economical method for the diagnosis of breast cancer. Breast imaging reporting and data system (BI-RADS) category 4 has the highest false-positive value of about 30% among five categories. The classification task in BI-RADS category 4 is challenging and has not been fully studied. PURPOSE This work aimed to use convolutional neural networks (CNNs) for breast tumor classification using B-mode images in category 4 to overcome the dependence on operator and artifacts. Additionally, this work intends to take full advantage of morphological and textural features in breast tumor US images to improve classification accuracy. METHODS First, original US images coming directly from the hospital were cropped and resized. In 1385 B-mode US BI-RADS category 4 images, the biopsy eliminated 503 samples of benign tumor and left 882 of malignant. Then, K-means clustering algorithm and entropy of sliding windows of US images were conducted. Considering the diversity of different characteristic information of malignant and benign represented by original B-mode images, K-means clustering images and entropy images, they are fused in a three-channel form multi-feature fusion images dataset. The training, validation, and test sets are 969, 277, and 139. With transfer learning, 11 CNN models including DenseNet and ResNet were investigated. Finally, by comparing accuracy, precision, recall, F1-score, and area under curve (AUC) of the results, models which had better performance were selected. The normality of data was assessed by Shapiro-Wilk test. DeLong test and independent t-test were used to evaluate the significant difference of AUC and other values. False discovery rate was utilized to ultimately evaluate the advantages of CNN with highest evaluation metrics. In addition, the study of anti-log compression was conducted but no improvement has shown in CNNs classification results. RESULTS With multi-feature fusion images, DenseNet121 has highest accuracy of 80.22 ± 1.45% compared to other CNNs, precision of 77.97 ± 2.89% and AUC of 0.82 ± 0.01. Multi-feature fusion improved accuracy of DenseNet121 by 1.87% from classification of original B-mode images (p < 0.05). CONCLUSION The CNNs with multi-feature fusion show a good potential of reducing the false-positive rate within category 4. The work illustrated that CNNs and fusion images have the potential to reduce false-positive rate in breast tumor within US BI-RADS category 4, and make the diagnosis of category 4 breast tumors to be more accurate and precise.
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Affiliation(s)
- Pengfei Xu
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jing Zhao
- The Second Hospital of Jilin University, Changchun, China
| | - Mingxi Wan
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Qing Song
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Qiang Su
- Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Diya Wang
- Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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20
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Ma DJ, Yang Y, Harguindeguy N, Tian Y, Small SA, Liu F, Rothman DL, Guo J. Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning. J Magn Reson Imaging 2024; 59:964-975. [PMID: 37401726 DOI: 10.1002/jmri.28868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). PURPOSE To investigate a convolutional neural network-based SR (CNN-SR) approach for simultaneous frequency-and-phase correction (FPC) of single-voxel Meshcher-Garwood point-resolved spectroscopy (MEGA-PRESS) MRS data. STUDY TYPE Retrospective. SUBJECTS Forty thousand simulated MEGA-PRESS datasets generated from FID Appliance (FID-A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA-PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. FIELD STRENGTH/SEQUENCE 3T, MEGA-PRESS. ASSESSMENT Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were -20 to 20 Hz and -90° to 90° and were uniformly distributed for the simulation dataset at different signal-to-noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0-5 Hz; 0-20°), medium offsets (5-10 Hz; 20-45°), and large offsets (10-20 Hz; 45-90°). STATISTICAL TESTS Two-tailed paired t-tests for model performances in the simulation and in vivo datasets were used and a P-value <0.05 was considered statistically significant. RESULTS CNN-SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN-SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, -0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied. DATA CONCLUSION The proposed CNN-SR method is an efficient and accurate approach for simultaneous FPC of single-voxel MEGA-PRESS MRS data. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- David J Ma
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Yanting Yang
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Natalia Harguindeguy
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Ye Tian
- Department of Biomedical Engineering, Columbia University, New York, New York, USA
| | - Scott A Small
- Department of Psychiatry, Columbia University, New York, New York, USA
- Department of Neurology, Columbia University, New York, New York, USA
- Taub Institute Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, New York, USA
| | - Feng Liu
- Department of Psychiatry, Columbia University, New York, New York, USA
- Columbia University Irving Medical Center, Columbia University, New York State Psychiatric Institute, New York, New York, USA
| | - Douglas L Rothman
- Radiology and Biomedical Imaging of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
| | - Jia Guo
- Department of Psychiatry, Columbia University, New York, New York, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, USA
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Şeker M, Özerdem MS. Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series. J Neurosci Methods 2024; 403:110057. [PMID: 38215948 DOI: 10.1016/j.jneumeth.2024.110057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/25/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024]
Abstract
BACKGROUND Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automatic and intelligent methods are required for the analysis and processing of EEG signals. NEW METHODS This paper aims to address the challenges associated with MCI diagnosis by leveraging EEG signals and deep learning techniques. The analysis in this study focuses on processing the information embedded within the sequence of raw EEG time series data. EEG recordings are collected from 10 Healthy Controls (HC) and 10 MCI participants using 19 electrodes during a 30 min eyes-closed session. EEG time series are transformed into 2 separate formats of input tensors and applied to deep neural network architectures. Convolutional Neural Network (CNN) and ResNet from scratch are performed with 2D time series with different segment lengths. Furthermore, EEGNet and DeepConvNet architectures are utilized for 1D time series. RESULTS ResNet demonstrates superior effectiveness in detecting MCI when compared to CNN architecture. Complete discrimination is achieved using EEGNet and DeepConvNet for noisy segments. COMPARISON WITH EXISTING METHODS ResNet has yielded a 3 % higher accuracy rate compared to CNN. None of the architectures in the literature have achieved 100 % accuracy except proposed EEGNet and DeepConvnet. CONCLUSION Deep learning architectures hold great promise in enhancing the accuracy of early MCI detection.
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Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
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22
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Ben Nasr Barber F, Elloumi Oueslati A. Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model. J Genet Eng Biotechnol 2024; 22:100359. [PMID: 38494268 PMCID: PMC10903757 DOI: 10.1016/j.jgeb.2024.100359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND Examining functions and characteristics of DNA sequences is a highly challenging task. When it comes to the human genome, which is made up of exons and introns, this task is more challenging. Human exons and introns contain millions to billions of nucleotides, which contributes to the complexity observed in this sequences. Considering how complicated the subject of genomics is, it is obvious that using signal processing techniques and deep learning tools to build a strong predictive model can be very helpful for the development of the research of the human genome. RESULTS After representing human exons and introns with color images using Frequency Chaos Game Representation, two pre-trained convolutional neural network models (Resnet-50 and GoogleNet) and a proposed CNN model having 13 hidden layers were used to classify our obtained images. We have reached a value of 92% for the accuracy rate for Resnet-50 model in about 7 h for the execution time, a value of 91.5% for the accuracy rate for the GoogleNet model in 2 h and a half for the execution time. For our proposed CNN model, we have reached 91.6% for the accuracy rate in 2 h and 37 min. CONCLUSIONS Our proposed CNN model is faster than the Resnet-50 model in terms of execution time. It was able to slightly exceed the GoogleNet model for the accuracy rate value.
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Affiliation(s)
- Feriel Ben Nasr Barber
- Electrical Engineering Department, SITI Laboratory, National School of Engineers of Tunis (ENIT), BP37, Le Belvedere, 1002 Tunis, Tunisia; Electrical Engineering Department, National School of Engineers of Carthage (ENICarthage), Tunis, Tunisia.
| | - Afef Elloumi Oueslati
- Electrical Engineering Department, SITI Laboratory, National School of Engineers of Tunis (ENIT), BP37, Le Belvedere, 1002 Tunis, Tunisia; Electrical Engineering Department, National School of Engineers of Carthage (ENICarthage), Tunis, Tunisia.
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23
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Sindhura C, Al Fahim M, Yalavarthy PK, Gorthi S. Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage. Med Phys 2024; 51:1944-1956. [PMID: 37702932 DOI: 10.1002/mp.16714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 07/26/2023] [Accepted: 08/20/2023] [Indexed: 09/14/2023] Open
Abstract
PURPOSE To propose an automated approach for detecting and classifying Intracranial Hemorrhages (ICH) directly from sinograms using a deep learning framework. This method is proposed to overcome the limitations of the conventional diagnosis by eliminating the time-consuming reconstruction step and minimizing the potential noise and artifacts that can occur during the Computed Tomography (CT) reconstruction process. METHODS This study proposes a two-stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework. The first stage of the framework is Intensity Transformed Sinogram Sythesizer, which synthesizes sinograms that are equivalent to the intensity-transformed CT images. The second stage comprises of a cascaded Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model that detects and classifies hemorrhages from the synthesized sinograms. The CNN module extracts high-level features from each input sinogram, while the RNN module provides spatial correlation of the neighborhood regions in the sinograms. The proposed method was evaluated on a publicly available RSNA dataset consisting of a large sample size of 8652 patients. RESULTS The results showed that the proposed method had a notable improvement as high as 27% in patient-wise accuracies when compared to state-of-the-art methods like ResNext-101, Inception-v3 and Vision Transformer. Furthermore, the sinogram-based approach was found to be more robust to noise and offset errors in comparison to CT image-based approaches. The proposed model was also subjected to a multi-label classification analysis to determine the hemorrhage type from a given sinogram. The learning patterns of the proposed model were also examined for explainability using the activation maps. CONCLUSION The proposed sinogram-based approach can provide an accurate and efficient diagnosis of ICH without the need for the time-consuming reconstruction step and can potentially overcome the limitations of CT image-based approaches. The results show promising outcomes for the use of sinogram-based approaches in detecting hemorrhages, and further research can explore the potential of this approach in clinical settings.
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Affiliation(s)
- Chitimireddy Sindhura
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Mohammad Al Fahim
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
| | - Phaneendra K Yalavarthy
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
| | - Subrahmanyam Gorthi
- Department of Electrical Engineering, Indian Institute of Technology, Tirupati, India
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24
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Zhang Y, Shi Z, Wang H, Cui S, Zhang L, Liu J, Shan X, Liu Y, Fang L. LumVertCancNet: A novel 3D lumbar vertebral body cancellous bone location and segmentation method based on hybrid Swin-transformer. Comput Biol Med 2024; 171:108237. [PMID: 38422966 DOI: 10.1016/j.compbiomed.2024.108237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/12/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
Lumbar vertebral body cancellous bone location and segmentation is crucial in an automated lumbar spine processing pipeline. Accurate and reliable analysis of lumbar spine image is expected to advantage practical medical diagnosis and population-based analysis of bone strength. However, the design of automated algorithms for lumbar spine processing is demanding due to significant anatomical variations and scarcity of publicly available data. In recent years, convolutional neural network (CNN) and vision transformers (Vits) have been the de facto standard in medical image segmentation. Although adept at capturing global features, the inherent bias of locality and weight sharing of CNN constrains its capacity to model long-range dependency. In contrast, Vits excel at long-range dependency modeling, but they may not generalize well with limited datasets due to the lack of inductive biases inherent to CNN. In this paper, we propose a deep learning-based two-stage coarse-to-fine solution to address the problem of automatic location and segmentation of lumbar vertebral body cancellous bone. Specifically, in the first stage, a Swin-transformer based model is applied to predict the heatmap of lumbar vertebral body centroids. Considering the characteristic anatomical structure of lumbar spine, we propose a novel loss function called LumAnatomy loss, which enforces the order and bend of the predicted vertebral body centroids. To inherit the excellence of CNN and Vits while preventing their respective limitations, in the second stage, we propose an encoder-decoder network to segment the identified lumbar vertebral body cancellous bone, which consists of two parallel encoders, i.e., a Swin-transformer encoder and a CNN encoder. To enhance the combination of CNNs and Vits, we propose a novel multi-scale attention feature fusion module (MSA-FFM), which address issues that arise when fusing features given at different encoders. To tackle the issue of lack of data, we raise the first large-scale lumbar vertebral body cancellous bone segmentation dataset called LumVBCanSeg containing a total of 185 CT scans annotated at voxel level by 3 physicians. Extensive experimental results on the LumVBCanSeg dataset demonstrate the proposed algorithm outperform other state-of-the-art medical image segmentation methods. The data is publicly available at: https://zenodo.org/record/8181250. The implementation of the proposed method is available at: https://github.com/sia405yd/LumVertCancNet.
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Affiliation(s)
- Yingdi Zhang
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zelin Shi
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huan Wang
- Spine Surgery Department, ShengJing Hospital of China Medical University, Shenyang, China
| | - Shaoqian Cui
- Spine Surgery Department, ShengJing Hospital of China Medical University, Shenyang, China
| | - Lei Zhang
- Spine Surgery Department, ShengJing Hospital of China Medical University, Shenyang, China
| | - Jiachen Liu
- Spine Surgery Department, ShengJing Hospital of China Medical University, Shenyang, China
| | - Xiuqi Shan
- Spine Surgery Department, ShengJing Hospital of China Medical University, Shenyang, China
| | - Yunpeng Liu
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lei Fang
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
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25
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Tandon R, Agrawal S, Rathore NPS, Mishra AK, Jain SK. A systematic review on deep learning-based automated cancer diagnosis models. J Cell Mol Med 2024; 28:e18144. [PMID: 38426930 PMCID: PMC10906380 DOI: 10.1111/jcmm.18144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 12/08/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024] Open
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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Affiliation(s)
| | | | | | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology DepartmentUniversity of California Santa BarbaraSanta BarbaraCaliforniaUSA
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26
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Vahab N, Bonu T, Kuhlmann L, Ramialison M, Tyagi S. Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data. Comput Biol Med 2024; 171:108068. [PMID: 38354497 DOI: 10.1016/j.compbiomed.2024.108068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/30/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024]
Abstract
The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.
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Affiliation(s)
- Naima Vahab
- School of Computational Technologies, RMIT University, Melbourne VIC 3000, Australia; Department of Infectious Diseases, Alfred Hospital, Prahran VIC 3008, Australia
| | - Tarun Bonu
- Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Levin Kuhlmann
- Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | | | - Sonika Tyagi
- School of Computational Technologies, RMIT University, Melbourne VIC 3000, Australia; Department of Infectious Diseases, Alfred Hospital, Prahran VIC 3008, Australia.
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27
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Bertazioli D, Piazza M, Carlomagno C, Gualerzi A, Bedoni M, Messina E. An integrated computational pipeline for machine learning-driven diagnosis based on Raman spectra of saliva samples. Comput Biol Med 2024; 171:108028. [PMID: 38335817 DOI: 10.1016/j.compbiomed.2024.108028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
Raman Spectroscopy promises the ability to encode in spectral data the significant differences between biological samples belonging to patients affected by a disease and samples of healthy patients (controls). However, the decoding and interpretation of the Raman spectral fingerprint is still a difficult and time-consuming procedure even for domain experts. In this work, we test an end-to-end deep-learning diagnostic pipeline able to classify spectral data from saliva samples. The pipeline has been validated against the SARS-COV-2 Infection and for the screening of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. The proposed system can be used for the fast prototyping of promising non-invasive, cost and time-efficient diagnostic screening tests.
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Affiliation(s)
- Dario Bertazioli
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy
| | - Marco Piazza
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy.
| | - Cristiano Carlomagno
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Alice Gualerzi
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Marzia Bedoni
- IRCCS Fondazione Don Carlo Gnocchi ONL US, Via Capecelatro 66, Milan, 20148, Italy
| | - Enza Messina
- University of Milano-Bicocca, Viale Sarca 336, Milan, 20126, Italy
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28
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Wang Z, Yang C, Li B, Wu H, Xu Z, Feng Z. Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models. Front Public Health 2024; 12:1365942. [PMID: 38496387 PMCID: PMC10941340 DOI: 10.3389/fpubh.2024.1365942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country. Methods We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model. Results Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models). Conclusion The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.
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Affiliation(s)
- ZhenDe Wang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - ChunXiao Yang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - Bing Li
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - HongTao Wu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhen Xu
- Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China
| | - ZiJian Feng
- Chinese Preventive Medicine Association, Beijing, China
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29
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Xu D, Ando N. Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information. J Struct Biol 2024; 216:108072. [PMID: 38431179 DOI: 10.1016/j.jsb.2024.108072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/11/2024] [Accepted: 02/25/2024] [Indexed: 03/05/2024]
Abstract
Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).
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Affiliation(s)
- Da Xu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA
| | - Nozomi Ando
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA.
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30
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Maginga TJ, Masabo E, Bakunzibake P, Kim KS, Nsenga J. Using wavelet transform and hybrid CNN - LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection. Heliyon 2024; 10:e26647. [PMID: 38420424 PMCID: PMC10901083 DOI: 10.1016/j.heliyon.2024.e26647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14-21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4-5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.
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Affiliation(s)
| | - Emmanuel Masabo
- African Centre of Excellence in Internet of Things (ACEIoT) - University of Rwanda (UR), Rwanda
| | - Pierre Bakunzibake
- African Centre of Excellence in Internet of Things (ACEIoT) - University of Rwanda (UR), Rwanda
| | - Kwang Soo Kim
- Global Research and Development Business Centre (GRC-SNU) -Seoul National University (SNU), South Korea
| | - Jimmy Nsenga
- African Centre of Excellence in Internet of Things (ACEIoT) - University of Rwanda (UR), Rwanda
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Xu D, Ando N. Miffi: Improving the accuracy of CNN-based cryo-EM micrograph filtering with fine-tuning and Fourier space information. bioRxiv 2024:2023.12.08.570849. [PMID: 38405773 PMCID: PMC10888874 DOI: 10.1101/2023.12.08.570849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Efficient and high-accuracy filtering of cryo-electron microscopy (cryo-EM) micrographs is an emerging challenge with the growing speed of data collection and sizes of datasets. Convolutional neural networks (CNNs) are machine learning models that have been proven successful in many computer vision tasks, and have been previously applied to cryo-EM micrograph filtering. In this work, we demonstrate that two strategies, fine-tuning models from pretrained weights and including the power spectrum of micrographs as input, can greatly improve the attainable prediction accuracy of CNN models. The resulting software package, Miffi, is open-source and freely available for public use (https://github.com/ando-lab/miffi).
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Affiliation(s)
- Da Xu
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14850, USA
| | - Nozomi Ando
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14850, USA
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32
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Lilhore UK, Dalal S, Varshney N, Sharma YK, Rao KBVB, Rao VVRM, Alroobaea R, Simaiya S, Margala M, Chakrabarti P. Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model. Sci Rep 2024; 14:4533. [PMID: 38402249 PMCID: PMC10894236 DOI: 10.1038/s41598-024-54927-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 02/18/2024] [Indexed: 02/26/2024] Open
Abstract
Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.
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Affiliation(s)
- Umesh Kumar Lilhore
- Department of Computer Science & Engineering, Chandigarh University Gharuan Mohali, Gharuan, 140413, Punjab, India.
| | - Surjeet Dalal
- Amity School of Engineering and Technology, Amity University Haryana, Panchgaon, Haryana, India
| | - Neeraj Varshney
- Department of Computer Engineering and Applications GLA University, Mathura, India
| | - Yogesh Kumar Sharma
- Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur, Andhra Pradesh, India
| | - K B V Brahma Rao
- Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
| | - V V R Maheswara Rao
- Dept. of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, Andhra Pradesh, India, 534202
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
| | - Sarita Simaiya
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, 140413, India
| | - Martin Margala
- School of Computing and Informatics, University of Louisiana, Lafayette, USA
| | - Prasun Chakrabarti
- Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, 313601, Rajasthan, India
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Lin ZH, Lai QY, Li HY. A Machine-Learning Strategy to Detect Mura Defects in a Low-Contrast Image by Piecewise Gamma Correction. Sensors (Basel) 2024; 24:1484. [PMID: 38475020 DOI: 10.3390/s24051484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/02/2024] [Accepted: 02/21/2024] [Indexed: 03/14/2024]
Abstract
A detection and classification machine-learning model to inspect Thin Film Transistor Liquid Crystal Display (TFT-LCD) Mura is proposed in this study. To improve the capability of the machine-learning model to inspect panels' low-contrast grayscale images, piecewise gamma correction and a Selective Search algorithm are applied to detect and optimize the feature regions based on the Semiconductor Equipment and Materials International Mura (SEMU) specifications. In this process, matching the segment proportions to gamma values of piecewise gamma is a task that involves derivative-free optimization which is trained by adaptive particle swarm optimization. The detection accuracy rate (DAR) is approximately 93.75%. An enhanced convolutional neural network model is then applied to classify the Mura type through using the Taguchi experimental design method that identifies the optimal combination of the convolution kernel and the maximum pooling kernel sizes. A remarkable defect classification accuracy rate (CAR) of approximately 96.67% is ultimately achieved. The entire defect detection and classification process can be completed in about 3 milliseconds.
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Affiliation(s)
- Zo-Han Lin
- Department of Mold and Die Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan
| | - Qi-Yuan Lai
- Department of Mold and Die Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan
| | - Hung-Yuan Li
- Department of Mold and Die Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan
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Moon JH, Choi G, Kim YH, Kim WY. PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks. Sensors (Basel) 2024; 24:1467. [PMID: 38475003 DOI: 10.3390/s24051467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are relatively small. Additionally, a limited amount of fine-grained crack data is available. To address this data-intensive problem, we propose a parallel dual encoder network fusing Pre-Conv-based Transformers and convolutional neural networks (PCTC-Net). The Pre-Conv module automatically optimizes each color channel with a small spatial kernel before the input of the transformer. The proposed model, PCTC-Net, was tested with the DeepCrack, Crack500, and Crackseg9k datasets. The experimental results showed that our model achieved higher generalization performance, stability, and F1 scores than the SOTA model DTrC-Net.
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Affiliation(s)
- Ji-Hwan Moon
- Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Gyuho Choi
- Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Yu-Hwan Kim
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Won-Yeol Kim
- Department of Artificial Intelligence Engineering, Chosun University, Gwangju 61452, Republic of Korea
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35
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Liu L, Wei Y, Tan Z, Zhang Q, Sun J, Zhao Q. Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network. Interdiscip Sci 2024:10.1007/s12539-024-00616-z. [PMID: 38381315 DOI: 10.1007/s12539-024-00616-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .
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Affiliation(s)
- Liwei Liu
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
- Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, 571158, China
| | - Yixin Wei
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Zhebin Tan
- College of Software, Dalian Jiaotong University, Dalian, 116028, China
| | - Qi Zhang
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
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36
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Karakaya O, Kilimci ZH. An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM. PeerJ Comput Sci 2024; 10:e1831. [PMID: 38435607 PMCID: PMC10909209 DOI: 10.7717/peerj-cs.1831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/31/2023] [Indexed: 03/05/2024]
Abstract
Anticancer peptides (ACPs) are a group of peptides that exhibit antineoplastic properties. The utilization of ACPs in cancer prevention can present a viable substitute for conventional cancer therapeutics, as they possess a higher degree of selectivity and safety. Recent scientific advancements generate an interest in peptide-based therapies which offer the advantage of efficiently treating intended cells without negatively impacting normal cells. However, as the number of peptide sequences continues to increase rapidly, developing a reliable and precise prediction model becomes a challenging task. In this work, our motivation is to advance an efficient model for categorizing anticancer peptides employing the consolidation of word embedding and deep learning models. First, Word2Vec, GloVe, FastText, One-Hot-Encoding approaches are evaluated as embedding techniques for the purpose of extracting peptide sequences. Then, the output of embedding models are fed into deep learning approaches CNN, LSTM, BiLSTM. To demonstrate the contribution of proposed framework, extensive experiments are carried on widely-used datasets in the literature, ACPs250 and independent. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed combination, FastText+BiLSTM, exhibits 92.50% of accuracy for ACPs250 dataset, and 96.15% of accuracy for the Independent dataset, thence determining new state-of-the-art.
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Affiliation(s)
- Onur Karakaya
- Research and Development Inc., Turkcell Technology, İstanbul, Turkey
| | - Zeynep Hilal Kilimci
- Department of Information Systems Engineering, Kocaeli University, Kocaeli, Turkey
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37
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Islam R, Majurski P, Kwon J, Sharma A, Tummala SRSK. Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks. Sensors (Basel) 2024; 24:1329. [PMID: 38400487 PMCID: PMC10892219 DOI: 10.3390/s24041329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures.
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Affiliation(s)
- Riadul Islam
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Patrick Majurski
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Jun Kwon
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Anurag Sharma
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Sri Ranga Sai Krishna Tummala
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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38
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Choi K. Block Partitioning Information-Based CNN Post-Filtering for EVC Baseline Profile. Sensors (Basel) 2024; 24:1336. [PMID: 38400494 PMCID: PMC10892633 DOI: 10.3390/s24041336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
The need for efficient video coding technology is more important than ever in the current scenario where video applications are increasing worldwide, and Internet of Things (IoT) devices are becoming widespread. In this context, it is necessary to carefully review the recently completed MPEG-5 Essential Video Coding (EVC) standard because the EVC Baseline profile is customized to meet the specific requirements needed to process IoT video data in terms of low complexity. Nevertheless, the EVC Baseline profile has a notable disadvantage. Since it is a codec composed only of simple tools developed over 20 years, it tends to represent numerous coding artifacts. In particular, the presence of blocking artifacts at the block boundary is regarded as a critical issue that must be addressed. To address this, this paper proposes a post-filter using a block partitioning information-based Convolutional Neural Network (CNN). The proposed method in the experimental results objectively shows an approximately 0.57 dB for All-Intra (AI) and 0.37 dB for Low-Delay (LD) improvements in each configuration by the proposed method when compared to the pre-post-filter video, and the enhanced PSNR results in an overall bitrate reduction of 11.62% for AI and 10.91% for LD in the Luma and Chroma components, respectively. Due to the huge improvement in the PSNR, the proposed method significantly improved the visual quality subjectively, particularly in blocking artifacts at the coding block boundary.
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Affiliation(s)
- Kiho Choi
- Department of Electronics Engineering, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Republic of Korea;
- Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin-si 17104, Gyeonggi-do, Republic of Korea
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39
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Jiang D, Wang K, Li H, Zhang Y. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign. Sensors (Basel) 2024; 24:1245. [PMID: 38400402 PMCID: PMC10893441 DOI: 10.3390/s24041245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024]
Abstract
This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.
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Affiliation(s)
- Dapeng Jiang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Keqi Wang
- College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China; (D.J.); (K.W.)
| | - Hongbo Li
- College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China;
| | - Yizhuo Zhang
- College of Computer Science and Artificial Intelligence, Changzhou University, 1 Gehu Middle Rd., Changzhou 213164, China
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40
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Dev DG, Bhatnagar V, Bhati BS, Gupta M, Nanthaamornphong A. LSTM CNN: A hybrid machine learning model to unmask fake news. Heliyon 2024; 10:e25244. [PMID: 38322966 PMCID: PMC10844048 DOI: 10.1016/j.heliyon.2024.e25244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 01/23/2024] [Indexed: 02/08/2024] Open
Abstract
The widespread dissemination of false information across various online platforms has emerged as a matter of paramount concern due to the potential harm it poses to individuals, communities, and entire nations. Substantial efforts are currently underway in the research community to combat this issue. A burgeoning area of study gaining significant traction is the development of fake news identification techniques. However, this field faces formidable challenges primarily stemming from limited resources, including access to comprehensive datasets, computational resources, and evaluation tools. To overcome these challenges, researchers are exploring various methodologies. One promising approach involves the use of feature abstraction and vectorization techniques. In this context, we highly recommend utilizing the Python sci-kit-learn module, which offers many invaluable tools such as the Count Vectorizer and Tiff Vectorizer. These tools enable the efficient handling of text data by converting it into numerical representations, thereby facilitating subsequent analysis. Once the text data is appropriately transformed, the next crucial step involves feature selection. To achieve optimal results, researchers often employ feature selection methods based on misperception matrices. These methods allow for the exploration and selection of the most suitable features, which are essential for achieving the highest accuracy in fake news identification.
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Affiliation(s)
- Deepali Goyal Dev
- GGSIPU, AIACTR, Delhi and Assistant Professor, ABES Engineering College, Ghaziabad, UP, India
| | - Vishal Bhatnagar
- NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), New Delhi, India
| | | | - Manoj Gupta
- Department of Electrical Engineering, SOS-Engineering & Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur (Chhattisgarh), India
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41
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R Shihabuddin A, Beevi K S. Efficient mitosis detection: leveraging pre-trained faster R- CNN and cell-level classification. Biomed Phys Eng Express 2024; 10:025031. [PMID: 38357907 DOI: 10.1088/2057-1976/ad262f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
The assessment of mitotic activity is an integral part of the comprehensive evaluation of breast cancer pathology. Understanding the level of tumor dissemination is essential for assessing the severity of the malignancy and guiding appropriate treatment strategies. A pathologist must manually perform the intricate and time-consuming task of counting mitoses by examining biopsy slices stained with Hematoxylin and Eosin (H&E) under a microscope. Mitotic cells can be challenging to distinguish in H&E-stained sections due to limited available datasets and similarities among mitotic and non-mitotic cells. Computer-assisted mitosis detection approaches have simplified the whole procedure by selecting, detecting, and labeling mitotic cells. Traditional detection strategies rely on image processing techniques that apply custom criteria to distinguish between different aspects of an image. Additionally, the automatic feature extraction from histopathology images that exhibit mitosis using neural networks.Additionally, the possibility of automatically extracting features from histopathological images using deep neural networks was investigated. This study examines mitosis detection as an object detection problem using multiple neural networks. From a medical standpoint, mitosis at the tissue level was also investigated utilising pre-trained Faster R-CNN and raw image data. Experiments were done on the MITOS-ATYPIA- 14 dataset and TUPAC16 dataset, and the results were compared to those of other methods described in the literature.
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Affiliation(s)
- Abdul R Shihabuddin
- Centre For Artificial Intelligence, TKM College of Engineering, Karicode, Kollam, 691005, Kerala, India
| | - Sabeena Beevi K
- Department of Electrical and Electronics Engineering, TKM College of Engineering, Karicode, Kollam, 691005, Kerala, India
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42
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Wang S, Wu J, Chen M, Huang S, Huang Q. Balanced transformer: efficient classification of glioblastoma and primary central nervous system lymphoma. Phys Med Biol 2024; 69:045032. [PMID: 38232389 DOI: 10.1088/1361-6560/ad1f88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 01/17/2024] [Indexed: 01/19/2024]
Abstract
Objective.Primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) are malignant primary brain tumors with different biological characteristics. Great differences exist between the treatment strategies of PCNSL and GBM. Thus, accurately distinguishing between PCNSL and GBM before surgery is very important for guiding neurosurgery. At present, the spinal fluid of patients is commonly extracted to find tumor markers for diagnosis. However, this method not only causes secondary injury to patients, but also easily delays treatment. Although diagnosis using radiology images is non-invasive, the morphological features and texture features of the two in magnetic resonance imaging (MRI) are quite similar, making distinction with human eyes and image diagnosis very difficult. In order to solve the problem of insufficient number of samples and sample imbalance, we used data augmentation and balanced sample sampling methods. Conventional Transformer networks use patch segmentation operations to divide images into small patches, but the lack of communication between patches leads to unbalanced data layers.Approach.To address this problem, we propose a balanced patch embedding approach that extracts high-level semantic information by reducing the feature dimensionality and maintaining the geometric variation invariance of the features. This approach balances the interactions between the information and improves the representativeness of the data. To further address the imbalance problem, the balanced patch partition method is proposed to increase the receptive field by sampling the four corners of the sliding window and introducing a linear encoding component without increasing the computational effort, and designed a new balanced loss function.Main results.Benefiting from the overall balance design, we conducted an experiment using Balanced Transformer and obtained an accuracy of 99.89%, sensitivity of 99.74%, specificity of 99.73% and AUC of 99.19%, which is far higher than the previous results (accuracy of 89.6% ∼ 96.8%, sensitivity of 74.3% ∼ 91.3%, specificity of 88.9% ∼ 96.02% and AUC of 87.8% ∼ 94.9%).Significance.This study can accurately distinguish PCNSL and GBM before surgery. Because GBM is a common type of malignant tumor, the 1% improvement in accuracy has saved many patients and reduced treatment times considerably. Thus, it can provide doctors with a good basis for auxiliary diagnosis.
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Affiliation(s)
- Shigang Wang
- Department of Electronic Engineering, College of Communication Engineering, Jilin University, Changchun 130012, People's Republic of China
| | - Jinyang Wu
- Department of Electronic Engineering, College of Communication Engineering, Jilin University, Changchun 130012, People's Republic of China
| | - Meimei Chen
- Department of Electronic Engineering, College of Communication Engineering, Jilin University, Changchun 130012, People's Republic of China
| | - Sa Huang
- Department of Radiology, the Second Hospital of Jilin University, Changchun 130012, People's Republic of China
| | - Qian Huang
- Department of Radiology, the Second Hospital of Jilin University, Changchun 130012, People's Republic of China
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43
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Helwan A, Azar D, Ma'aitah MKS. Conventional and deep learning methods in heart rate estimation from RGB face videos. Physiol Meas 2024; 45:02TR01. [PMID: 38081130 DOI: 10.1088/1361-6579/ad1458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/11/2023] [Indexed: 02/10/2024]
Abstract
Contactless vital signs monitoring is a fast-advancing scientific field that aims to employ monitoring methods that do not necessitate the use of leads or physical attachments to the patient in order to overcome the shortcomings and limits of traditional monitoring systems. Several traditional methods have been applied to extract the heart rate (HR) signal from the face. Moreover, machine learning has recently contributed majorly to the development of such a field in which deep networks and other deep learning methods are employed to extract the HR signal from RGB face videos. In this paper, we evaluate the state-of-the-art conventional and deep learning methods for HR estimates, focusing on the limits of deep learning methods and the availability of less-controlled face video datasets. We aim to present an extensive review that helps the various approaches of remote photoplethysmography extraction and HR estimation to be understood, in addition to their drawbacks and benefits.
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Affiliation(s)
| | | | - Mohamad Khaleel Sallam Ma'aitah
- Department of Robotics and Artificial Intelligence Engineering, Faculty of Engineering & Technology, Applied Science Private University, Amman, Jordan
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44
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Li M, Gong Y, Zheng Z. Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism. Sensors (Basel) 2024; 24:1132. [PMID: 38400290 PMCID: PMC10892868 DOI: 10.3390/s24041132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/04/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method named Let-Net (large kernel and attention mechanism network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to capture a broader spectrum of spatial contextual information, utilizing deep convolution in conjunction with residual connections to curtail the volume of model parameters. Subsequently, an integrated attention mechanism is applied to augment information flow within the channel and spatial dimensions, effectively modeling global information for the extraction of crucial FV features. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.
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Affiliation(s)
- Meihui Li
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China;
- Jiangsu Engineering Laboratory of Cyberspace Security, Suzhou 215006, China
| | - Yufei Gong
- School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
| | - Zhaohui Zheng
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China;
- Jiangsu Engineering Laboratory of Cyberspace Security, Suzhou 215006, China
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45
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Iriya R, Braswell B, Mo M, Zhang F, Haydel SE, Wang S. Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy. Biosensors (Basel) 2024; 14:89. [PMID: 38392008 PMCID: PMC10887190 DOI: 10.3390/bios14020089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/02/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024]
Abstract
Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel large-volume microscopy (LVM) system for rapid, point-of-care bacterial detection. This system, using low magnification (1-2×), visualizes sufficient sample volumes, eliminating the need for culture-based enrichment. Employing deep neural networks, our model demonstrates superior accuracy in detecting uropathogenic Escherichia coli compared to traditional machine learning methods. Future endeavors will focus on enriching our datasets with mixed samples and a broader spectrum of uropathogens, aiming to extend the applicability of our model to clinical samples.
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Affiliation(s)
- Rafael Iriya
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
- School of Electrical and Computer Engineering, Tempe, AZ 85287, USA
| | - Brandyn Braswell
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Manni Mo
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Fenni Zhang
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
| | - Shelley E. Haydel
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Shaopeng Wang
- Biodesign Center for Biosensors and Bioelectronics, Arizona State University, Tempe, AZ 85287, USA; (R.I.); (B.B.); (M.M.); (F.Z.); (S.E.H.)
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
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Polat O, Türkoğlu M, Polat H, Oyucu S, Üzen H, Yardımcı F, Aksöz A. Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems. Sensors (Basel) 2024; 24:1040. [PMID: 38339756 PMCID: PMC10857162 DOI: 10.3390/s24031040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/29/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.
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Affiliation(s)
- Onur Polat
- Department of Computer Engineering, Bingöl University, Bingöl 12000, Turkey;
| | - Muammer Türkoğlu
- Department of Software Engineering, Samsun University, Samsun 55000, Turkey;
| | - Hüseyin Polat
- Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06500, Turkey;
| | - Saadin Oyucu
- Department of Computer Engineering, Adiyaman University, Adiyaman 02040, Turkey;
| | - Hüseyin Üzen
- Department of Computer Engineering, Bingöl University, Bingöl 12000, Turkey;
| | | | - Ahmet Aksöz
- MOBILERS, Sivas Cumhuriyet University, Sivas 58580, Turkey;
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Aslan M, Baykara M, Alakus TB. LieWaves: dataset for lie detection based on EEG signals and wavelets. Med Biol Eng Comput 2024:10.1007/s11517-024-03021-2. [PMID: 38311647 DOI: 10.1007/s11517-024-03021-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024]
Abstract
This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.
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Affiliation(s)
- Musa Aslan
- Department of Software Engineering, Karadeniz Technical University, Trabzon, Turkey
| | - Muhammet Baykara
- Department of Software Engineering, Firat University, Elazig, Turkey
| | - Talha Burak Alakus
- Department of Software Engineering, Kirklareli University, Kirklareli, Turkey.
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Nishino K. Skin patch based makeup finish assessment technique by deep neural network. Skin Res Technol 2024; 30:e13561. [PMID: 38297920 PMCID: PMC10831195 DOI: 10.1111/srt.13561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Skin color and texture play a significant role in influencing impressions. To understand the influence of skin appearance and to develop better makeup products, objective evaluation methods for makeup finish have been explored. This study aims to apply machine learning technology, specifically deep neural network (DNN), to accurately analyze and evaluate delicate and complex cosmetic skin textures. METHODS "Skin patch datasets" were extracted from facial images and used to train a DNN model. The advantages of using skin patches include retaining fine texture, eliminating false correlations from non-skin features, and enabling visualization of the inferred results for the entire face. The DNN was trained in two ways: a classification task to classify skin attributes and a regression task to predict the visual assessment of experts. The trained DNNs were applied for the evaluation of actual makeup conditions. RESULTS In the classification task training, skin patch-based classifiers for age range, presence or absence of base makeup, formulation type (powder/liquid) of the applied base makeup, and immediate/while after makeup application were developed. The trained DNNs on regression task showed high prediction accuracy for the experts' visual assessment. Application of DNN to the evaluation of actual makeup conditions clearly showed appropriate evaluation results in line with the appearance of the makeup finish. CONCLUSION The proposed method of using DNNs trained on skin patches effectively evaluates makeup finish. This approach has potential applications in visual science research and cosmetics development. Further studies can explore the analysis of different skin conditions and the development of personalized cosmetics.
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Affiliation(s)
- Ken Nishino
- Makeup Products ResearchKao CorporationOdawaraKanagawaJapan
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Kumar S, Kumar H, Kumar G, Singh SP, Bijalwan A, Diwakar M. A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review. BMC Med Imaging 2024; 24:30. [PMID: 38302883 PMCID: PMC10832080 DOI: 10.1186/s12880-024-01192-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/03/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. OBJECTIVE This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. METHODS The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. RESULTS This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.
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Affiliation(s)
- Sunil Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
- Department of Information Technology, School of Engineering and Technology (UIET), CSJM University, Kanpur, India
| | - Harish Kumar
- Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India
| | - Gyanendra Kumar
- Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India
| | | | - Anchit Bijalwan
- Faculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia.
| | - Manoj Diwakar
- Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, India
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Tegegne AM, Lohani TK, Eshete AA. Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia. Environ Res 2024; 242:117790. [PMID: 38036202 DOI: 10.1016/j.envres.2023.117790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 11/09/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023]
Abstract
Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resource is now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN based AUC validation platform showed that, 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration, and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.
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Affiliation(s)
| | - Tarun Kumar Lohani
- Arba Minch Water Technology Institute, Arba Minch University, Arba Minch, Ethiopia
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