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Kamireddy RR, Kandala RNVPS, Dhuli R, Polinati S, Sonti K, Tadeusiewicz R, Pławiak P. Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding. PLoS One 2024; 19:e0306492. [PMID: 39088437 PMCID: PMC11293751 DOI: 10.1371/journal.pone.0306492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 06/18/2024] [Indexed: 08/03/2024] Open
Abstract
Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.
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Affiliation(s)
| | | | - Ravindra Dhuli
- School of Electronics Engineering (SENSE), VIT-AP University, Amaravati, Andhra Pradesh, India
| | | | - Kamesh Sonti
- Department of ECE, SVEC, Tadepalligudem, Andhra Pradesh, India
| | - Ryszard Tadeusiewicz
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland
| | - Paweł Pławiak
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
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2
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Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng 2024; 52:1159-1183. [PMID: 38383870 DOI: 10.1007/s10439-024-03459-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024]
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India
| | - Sania Thomas
- Department of Computer Science and Engineering, Saintgits College of Engineering, Kottayam, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - J Arun
- Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, India
| | - S Naveen
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - S Madhu
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
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3
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Vidyarthi A. Probabilistic hierarchical clustering based identification and segmentation of brain tumors in magnetic resonance imaging. BIOMED ENG-BIOMED TE 2024; 69:181-192. [PMID: 37871189 DOI: 10.1515/bmt-2021-0313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 10/11/2023] [Indexed: 10/25/2023]
Abstract
The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice complete=80.5 %, Dice core=73.2 %, and Dice enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.
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Affiliation(s)
- Ankit Vidyarthi
- Department of CSE & IT, Jaypee Institute of Technology, Noida, India
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4
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Luximon DC, Neylon J, Ritter T, Agazaryan N, Hegde JV, Steinberg ML, Low DA, Lamb JM. Results of an Artificial Intelligence-Based Image Review System to Detect Patient Misalignment Errors in a Multi-institutional Database of Cone Beam Computed Tomography-Guided Radiation Therapy. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00392-4. [PMID: 38485098 DOI: 10.1016/j.ijrobp.2024.02.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/15/2024] [Accepted: 02/28/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE Present knowledge of patient setup and alignment errors in image guided radiation therapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible owing to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate. METHODS AND MATERIALS The EDA was developed to analyze the registration between planning scans and pretreatment cone beam computed tomography scans, outputting a misalignment score ranging from 0 (most unlikely) to 1 (most likely). The algorithm was trained using simulated translational errors on a data set obtained from 680 patients treated at 2 radiation therapy clinics between 2017 and 2022. A receiver operating characteristic analysis was performed to obtain target thresholds. DICOM Query and Retrieval software was integrated with the EDA to interact with the clinical database and fully automate data retrieval and analysis during a retrospective error search from 2016 to 2017 and from 2021 to 2022 for the 2 institutions, respectively. Registrations were flagged for human review using both a hard-thresholding method and a prediction trending analysis over each individual patient's treatment course. Flagged registrations were manually reviewed and categorized as errors (>1 cm misalignment at the target) or nonerrors. RESULTS A total of 17,612 registrations were analyzed by the EDA, resulting in 7.7% flagged events. Three previously reported errors were successfully flagged by the EDA, and 4 previously unreported vertebral body misalignment errors were discovered during case reviews. False positive cases often displayed substantial image artifacts, patient rotation, and soft tissue anatomy changes. CONCLUSIONS Our results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of IGRT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction.
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Affiliation(s)
- Dishane C Luximon
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California.
| | - Jack Neylon
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Timothy Ritter
- Department of Medical Physics, Virginia Commonwealth University, Richmond, Virginia
| | - Nzhde Agazaryan
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - John V Hegde
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Michael L Steinberg
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California
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5
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Sailunaz K, Alhajj S, Özyer T, Rokne J, Alhajj R. A survey on brain tumor image analysis. Med Biol Eng Comput 2024; 62:1-45. [PMID: 37700082 DOI: 10.1007/s11517-023-02873-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/20/2023] [Indexed: 09/14/2023]
Abstract
Medical imaging, also known as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. Medical imaging procedures include non-invasive tests that allow doctors to diagnose injuries and diseases without being intrusive TechTarget (n.d.). A number of tools and techniques are used to automate the analysis of medical images acquired with various image processing methods. The brain is one of the largest and most complex organs of the human body and anomaly detection from brain images (i.e., MRI, CT, PET, etc.) is one of the major research areas of medical image analysis. Image processing methods such as filtering and thresholding models, geometry models, graph models, region-based analysis, connected component analysis, machine learning (ML) models, the recent deep learning (DL) models, and various hybrid models are used in brain image analysis. Brain tumors are one of the most common brain diseases with a high mortality rate, and it is difficult to analyze from brain images for the versatility of the shape, location, size, texture, and other characteristics. In this paper, a comprehensive review on brain tumor image analysis is presented with basic ideas of brain tumor, brain imaging, brain image analysis tasks, brain image analysis models, brain tumor image features, performance metrics used for evaluating the models, and some available datasets on brain tumor/medical images. Some challenges of brain tumor analysis are also discussed including suggestions for future research directions. The graphical abstract summarizes the contributions of this paper.
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Affiliation(s)
- Kashfia Sailunaz
- Department of Computer Science, University of Calgary, Alberta, Canada
| | - Sleiman Alhajj
- International School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Tansel Özyer
- Department of Computer Engineering, Ankara Medipol University, Ankara, Turkey
| | - Jon Rokne
- Department of Computer Science, University of Calgary, Alberta, Canada
| | - Reda Alhajj
- Department of Computer Science, University of Calgary, Alberta, Canada.
- Department of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
- Department of Health Informatics, University of Southern Denmark, Odense, Denmark.
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6
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Shen L, Zhang Y, Wang Q, Qin F, Sun D, Min H, Meng Q, Xu C, Zhao W, Song X. Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation. PLoS One 2023; 18:e0288658. [PMID: 37440581 DOI: 10.1371/journal.pone.0288658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution.
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Affiliation(s)
- Longfeng Shen
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Big-Data Research Center on University Management, Huaibei, Anhui, China
| | - Yingjie Zhang
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Qiong Wang
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Fenglan Qin
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Dengdi Sun
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Artificial Intelligence, Anhui University, Hefei, China
| | - Hai Min
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China
| | - Qianqian Meng
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
| | - Chengzhen Xu
- Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB), College of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
| | - Wei Zhao
- Huaibei People's Hospital, Huaibei, Anhui, China
| | - Xin Song
- Huaibei People's Hospital, Huaibei, Anhui, China
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7
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Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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8
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Anagun Y. Smart brain tumor diagnosis system utilizing deep convolutional neural networks. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-27. [PMID: 37362644 PMCID: PMC10140727 DOI: 10.1007/s11042-023-15422-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/12/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
The early diagnosis of cancer is crucial to provide prompt and adequate management of the diseases. Imaging tests, in particular magnetic resonance imaging (MRI), are the first preferred method for diagnosis. However, these tests have some limitations which can cause a delay in detection and diagnosis. The use of computer-aided intelligent systems can assist physicians in diagnosis. In this study, we established a Convolutional Neural Network (CNN)-based brain tumor diagnosis system using EfficientNetv2s architecture, which was improved with the Ranger optimization and extensive pre-processing. We also compared the proposed model with state-of-the-art deep learning architectures such as ResNet18, ResNet200d, and InceptionV4 in discriminating brain tumors based on their spatial features. We achieved the best micro-average results with 99.85% test accuracy, 99.89% Area under the Curve (AUC), 98.16% precision, 98.17% recall, and 98.21% f1-score. Furthermore, the experimental results of the improved model were compared to various CNN-based architectures using key performance metrics and were shown to have a strong impact on tumor categorization. The proposed system has been experimentally evaluated with different optimizers and compared with recent CNN architectures, on both augmented and original data. The results demonstrated a convincing performance in tumor detection and diagnosis.
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Affiliation(s)
- Yildiray Anagun
- Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir, Turkey
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9
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Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression. Life (Basel) 2023; 13:life13030691. [PMID: 36983845 PMCID: PMC10056696 DOI: 10.3390/life13030691] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/24/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare.
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Bashkandi AH, Sadoughi K, Aflaki F, Alkhazaleh HA, Mohammadi H, Jimenez G. Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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11
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Liu X, Hou S, Liu S, Ding W, Zhang Y. Attention-based Multimodal Glioma Segmentation with Multi-attention Layers for Small-intensity Dissimilarity. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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12
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Madhavan MV, Khamparia A, Pande SD. An augmented customized deep learning approach for brain tumour identification. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2023.2182382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Affiliation(s)
- Mangena Venu Madhavan
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, India
| | - Aditya Khamparia
- Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Satellite Center, Amethi, India
| | - Sagar Dhanraj Pande
- School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, India
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13
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Reddy KR, Dhuli R. A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images. Diagnostics (Basel) 2023; 13:diagnostics13020312. [PMID: 36673122 PMCID: PMC9858139 DOI: 10.3390/diagnostics13020312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/22/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
Over the last few years, brain tumor-related clinical cases have increased substantially, particularly in adults, due to environmental and genetic factors. If they are unidentified in the early stages, there is a risk of severe medical complications, including death. So, early diagnosis of brain tumors plays a vital role in treatment planning and improving a patient's condition. There are different forms, properties, and treatments of brain tumors. Among them, manual identification and classification of brain tumors are complex, time-demanding, and sensitive to error. Based on these observations, we developed an automated methodology for detecting and classifying brain tumors using the magnetic resonance (MR) imaging modality. The proposed work includes three phases: pre-processing, classification, and segmentation. In the pre-processing, we started with the skull-stripping process through morphological and thresholding operations to eliminate non-brain matters such as skin, muscle, fat, and eyeballs. Then we employed image data augmentation to improve the model accuracy by minimizing the overfitting. Later in the classification phase, we developed a novel lightweight convolutional neural network (lightweight CNN) model to extract features from skull-free augmented brain MR images and then classify them as normal and abnormal. Finally, we obtained infected tumor regions from the brain MR images in the segmentation phase using a fast-linking modified spiking cortical model (FL-MSCM). Based on this sequence of operations, our framework achieved 99.58% classification accuracy and 95.7% of dice similarity coefficient (DSC). The experimental results illustrate the efficiency of the proposed framework and its appreciable performance compared to the existing techniques.
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14
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Kalam R, Thomas C, Rahiman MA. Brain tumor detection in MRI images using Adaptive-ANFIS classifier with segmentation of tumor and edema. Soft comput 2022. [DOI: 10.1007/s00500-022-07687-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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15
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Premachandran S, Haldavnekar R, Das S, Venkatakrishnan K, Tan B. DEEP Surveillance of Brain Cancer Using Self-Functionalized 3D Nanoprobes for Noninvasive Liquid Biopsy. ACS NANO 2022; 16:17948-17964. [PMID: 36112671 DOI: 10.1021/acsnano.2c04187] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain cancers, one of the most fatal malignancies, require accurate diagnosis for guided therapeutic intervention. However, conventional methods for brain cancer prognosis (imaging and tissue biopsy) face challenges due to the complex nature and inaccessible anatomy of the brain. Therefore, deep analysis of brain cancer is necessary to (i) detect the presence of a malignant tumor, (ii) identify primary or secondary origin, and (iii) find where the tumor is housed. In order to provide a diagnostic technique with such exhaustive information here, we attempted a liquid biopsy-based deep surveillance of brain cancer using a very minimal amount of blood serum (5 μL) in real time. We hypothesize that holistic analysis of serum can act as a reliable source for deep brain cancer surveillance. To identify minute amounts of tumor-derived material in circulation, we synthesized an ultrasensitive 3D nanosensor, adopted SERS as a diagnostic methodology, and undertook a DEEP neural network-based brain cancer surveillance. Detection of primary and secondary tumor achieved 100% accuracy. Prediction of intracranial tumor location achieved 96% accuracy. This modality of using patient sera for deep surveillance is a promising noninvasive liquid biopsy tool with the potential to complement current brain cancer diagnostic methodologies.
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Affiliation(s)
- Srilakshmi Premachandran
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Rupa Haldavnekar
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Sunit Das
- Scientist, St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Institute of Medical Sciences, Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Krishnan Venkatakrishnan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Ultrashort Laser Nanomanufacturing Research Facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
| | - Bo Tan
- Keenan Research Center for Biomedical Science, Unity Health Toronto, Toronto, Ontario M5B 1W8, Canada
- Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University (formerly Ryerson University) and St. Michael's Hospital, Toronto, Ontario M5B 1W8, Canada
- Nano Characterization Laboratory, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
- Nano-Bio Interface facility, Faculty of Engineering and Architectural Sciences, Toronto Metropolitan University (formerly Ryerson University), 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada
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16
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Qi T, Wang G. Superiority of quadratic over conventional neural networks for classification of gaussian mixture data. Vis Comput Ind Biomed Art 2022; 5:23. [PMID: 36167898 PMCID: PMC9515302 DOI: 10.1186/s42492-022-00118-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractTo enrich the diversity of artificial neurons, a type of quadratic neurons was proposed previously, where the inner product of inputs and weights is replaced by a quadratic operation. In this paper, we demonstrate the superiority of such quadratic neurons over conventional counterparts. For this purpose, we train such quadratic neural networks using an adapted backpropagation algorithm and perform a systematic comparison between quadratic and conventional neural networks for classificaiton of Gaussian mixture data, which is one of the most important machine learning tasks. Our results show that quadratic neural networks enjoy remarkably better efficacy and efficiency than conventional neural networks in this context, and potentially extendable to other relevant applications.
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A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7935346. [PMID: 36059415 PMCID: PMC9433214 DOI: 10.1155/2022/7935346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/09/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022]
Abstract
Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.
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18
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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10245-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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19
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Optimal Superpixel Kernel-Based Kernel Low-Rank and Sparsity Representation for Brain Tumour Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3514988. [PMID: 35785083 PMCID: PMC9249491 DOI: 10.1155/2022/3514988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 02/27/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022]
Abstract
Given the need for quantitative measurement and 3D visualisation of brain tumours, more and more attention has been paid to the automatic segmentation of tumour regions from brain tumour magnetic resonance (MR) images. In view of the uneven grey distribution of MR images and the fuzzy boundaries of brain tumours, a representation model based on the joint constraints of kernel low-rank and sparsity (KLRR-SR) is proposed to mine the characteristics and structural prior knowledge of brain tumour image in the spectral kernel space. In addition, the optimal kernel based on superpixel uniform regions and multikernel learning (MKL) is constructed to improve the accuracy of the pairwise similarity measurement of pixels in the kernel space. By introducing the optimal kernel into KLRR-SR, the coefficient matrix can be solved, which allows brain tumour segmentation results to conform with the spatial information of the image. The experimental results demonstrate that the segmentation accuracy of the proposed method is superior to several existing methods under different indicators and that the sparsity constraint for the coefficient matrix in the kernel space, which is integrated into the kernel low-rank model, has certain effects in preserving the local structure and details of brain tumours.
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20
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Fast Treetops Counting Using Mathematical Image Symmetry, Segmentation, and Fast K-Means Classification Algorithms. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Forests are important because they contribute to biodiversity, regulation of carbon dioxide, protection of hydrographic basins, wood production. This article presents a methodology for fast and effective counting of treetops using the mathematical symmetry of the grayscale image. For the treetop counting, the unsupervised k-means classification Algorithm was used with two groups or centroids: treetop and not-treetop. By using these groups and the mathematical symmetry of the image, a fast k-means classification Algorithm is generated. To solve the problem of treetop overlapping and perform a more accurate counting, the watershed Algorithm was used. This methodology has a mean treetop count accuracy of 98.3% with a confidence level of 99% in the interval (97.31, 99.7). Aerial images of the coniferous forest of Alcudia, Mallorca, Spain were used. Forests attenuate climatic changes originated by global warming. Drastic climatic changes cause catastrophes to humanity. This research would help the automatic, massive and recurring counting of treetops with the aim of obtaining forest inventories in order to take care of forests.
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21
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Thayumanavan M, Ramasamy A. Recurrent Neural Network Deep Learning Techniques for Brain Tumor Segmentation and Classification of Magnetic Resonance Imaging Images. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Brain Tumour is a one of the most threatful disease in the world. It reduces the life span of human beings. Computer vision is advantageous for human health research because it eliminates the need for human judgement to get accurate data. The most reliable and secure imaging techniques
for magnetic resonance imaging are CT scans, X-rays, and MRI scans (MRI). MRI can locate tiny objects. The focus of our paper will be the many techniques for detecting brain cancer using brain MRI. Early detection of tumour and diagnosis is might essential to radiologist to initiate better
treatment. MRI is a competent and speedy method of examining a brain tumour. Resonance in Magnetic Fields Imaging technology is a non-invasive technique that aids in the segmentation of brain tumour images. Deep learning algorithm delivers good outcomes in terms of reducing time consumption
and precise tumour diagnosis (solution). This research proposed that a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Supervised Deep Learning model be used to automatically find and split brain tumours. The RNN Model outperforms the CNN Model by 98.91 percentage. These
models categorize brain images as normal or pathological, and their performance was evaluated.
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Affiliation(s)
- Meenal Thayumanavan
- Department of ECE, Kongunadu College of Engineering and Technology, Trichy, 621215, Tamil Nadu, India
| | - Asokan Ramasamy
- Department of ECE, Kongunadu College of Engineering and Technology, Trichy, 621215, Tamil Nadu, India
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22
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Gorgani HH, Shabani S. Online exams and the COVID-19 pandemic: a hybrid modified FMEA, QFD, and k-means approach to enhance fairness. SN APPLIED SCIENCES 2021; 3:818. [PMID: 34604704 PMCID: PMC8477634 DOI: 10.1007/s42452-021-04805-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/15/2021] [Indexed: 11/25/2022] Open
Abstract
COVID-19 pandemic caused an increasing demand for online academic classes, which led to the demand for effective online exams with regards to limitations on time and resources. Consequently, holding online exams with sufficient reliability and effectiveness became one of the most critical and challenging subjects in higher education. Therefore, it is essential to have a preventive algorithm to allocate time and financial resources effectively. In the present study, a fair test with sufficient validity is first defined, and then by analogy with an engineering product, the design process is implemented on it. For this purpose, a hybrid method based on FMEA, which is a preventive method to identify potential failure modes and prioritize their risk, is employed. The method's output is provided to the QFD algorithm as the needs of product customers. Then, the proposed solutions to prevent failures are weighted and prioritized as the product's technical features. Some modifications are made to the classic form of FMEA in the proposed method to eliminate its deficiencies and contradictions. Therefore, our proposed algorithm is a precautionary approach that works to prevent breakdowns instead of fixing them following their occurrence. This issue is very effective in increasing the efficiency of activities in times of crisis. Eventually, a prioritized list of preventive actions is provided, allowing us to choose from available solutions in the circumstances with limited time and budgetary, where we cannot take all possible actions.
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Affiliation(s)
| | - Sharif Shabani
- Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran
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23
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Lotlikar VS, Satpute N, Gupta A. Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. Curr Med Imaging 2021; 18:604-622. [PMID: 34561990 DOI: 10.2174/1573405617666210923144739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/09/2021] [Accepted: 07/27/2021] [Indexed: 11/22/2022]
Abstract
According to the international agency for research on cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as magnetic resonance imaging (MRI) and computed tomography scans using a computer-aided design. Machine learning and deep learning techniques have gained significance among researchers in medical fields, especially convolutional neural networks (CNN), due to their ability to analyze large amounts of complex image data and perform classification. The objective of this review article is to present an exhaustive study of techniques such as preprocessing, machine learning, and deep learning that have been adopted in the last 15 years and based on it to present a detailed comparative analysis. The challenges encountered by researchers in the past for tumor detection have been discussed along with the future scopes that can be taken by the researchers as the future work. Clinical challenges that are encountered have also been discussed, which are missing in existing review articles.
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Affiliation(s)
- Venkatesh S Lotlikar
- MTech scholar, Department of E&TC Engineering, College of Engineering Pune, India
| | - Nitin Satpute
- Electrical and Computer Engineering, Aarhus University. Denmark
| | - Aditya Gupta
- Adjunct Faculty, Department of E&TC Engineering, College of Engineering Pune, India
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Abstract
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.
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25
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Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model. Diagnostics (Basel) 2021; 11:diagnostics11091589. [PMID: 34573931 PMCID: PMC8471235 DOI: 10.3390/diagnostics11091589] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/18/2021] [Accepted: 08/18/2021] [Indexed: 11/16/2022] Open
Abstract
The process of diagnosing brain tumors is very complicated for many reasons, including the brain's synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named "DWAE model", employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices' quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.
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26
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Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, Singh AK. Crop yield prediction integrating genotype and weather variables using deep learning. PLoS One 2021; 16:e0252402. [PMID: 34138872 PMCID: PMC8211294 DOI: 10.1371/journal.pone.0252402] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 05/16/2021] [Indexed: 11/19/2022] Open
Abstract
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.
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Affiliation(s)
- Johnathon Shook
- Department of Agronomy, Iowa State University, Ames, IA, United States of America
| | - Tryambak Gangopadhyay
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States of America
| | - Linjiang Wu
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States of America
| | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States of America
| | - Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States of America
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27
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Liu T, Yuan Z, Wu L, Badami B. An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm. Proc Inst Mech Eng H 2021; 235:459-469. [PMID: 33435847 DOI: 10.1177/0954411920987964] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Precise and timely detection of brain tumor area has a very high effect on the selection of medical care, its success rate and following the disease process during treatment. Existing algorithms for brain tumor diagnosis have problems in terms of better performance on various brain images with different qualities, low sensitivity of the results to the parameters introduced in the algorithm and also reliable diagnosis of tumors in the early stages of formation. A computer aided system is proposed in this research for automatic brain tumors diagnosis. The method includes four main parts: pre-processing and segmentation techniques, features extraction and final categorization. Gray-level co-occurrence matrix (GLCM) and Discrete Wavelet Transform (DWT) were applied for characteristic extraction of the MR images which are then injected to an optimized convolutional neural network (CNN) for the final diagnosis. The CNN is optimized by a new design of Sparrow Search Algorithm classification (ESSA). Finally, a comparison of the results of the method with three state of the art technique on the Whole Brain Atlas (WBA) database to show its higher efficiency.
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Affiliation(s)
- Tingting Liu
- Department of Oncology – Cardiology, Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Zhi Yuan
- Engineering Research Center of Renewable Energy Power Generation and Grid-Connected Control, Ministry of Education, Xinjiang University, Urumqi, Xinjiang, China
| | - Li Wu
- Department of Oncology – Cardiology, Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi, Xinjiang, China
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28
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Chitradevi D, Prabha S, Alex Daniel Prabhu. Diagnosis of Alzheimer disease in MR brain images using optimization techniques. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-04984-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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29
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Sun YS, Ou-Yang L, Dai DQ. LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data. Mol Omics 2020; 16:465-473. [PMID: 32572422 DOI: 10.1039/d0mo00034e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The development of single-cell RNA-sequencing (scRNA-seq) technologies brings tremendous opportunities for quantitative research and analyses at the cellular level. In particular, as a crucial task of scRNA-seq analysis, single cell clustering shines a light on natural groupings of cells to give new insights into the biological mechanisms and disease studies. However, it remains a challenge to identify cell clusters from lots of cell mixtures effectively and accurately. In this paper, we propose a novel adaptive joint clustering framework, named the low-rank self-representation K-means method (LRSK), to learn the data representation matrix and cluster indicator matrix jointly from scRNA-seq data. Specifically, instead of calculating the similarities among cells from the original data, we seek a low-rank representation of the original data to better reflect the underlying relationships among cells. Moreover, an Augmented Lagrangian Multiplier (ALM) based optimization algorithm is adopted to solve this problem. Experimental results on various scRNA-seq datasets and case studies demonstrate that our method performs better than other state-of-the-art single cell clustering algorithms. The analysis of unlabeled large single-cell liver cancer sequencing data further shows that our prediction results are more reasonable and interpretable.
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Affiliation(s)
- Ye-Sen Sun
- Intelligent Data Center, School of Mathematics, Sun Yat-sen University, Guangzhou, China.
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30
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Voice Pathology Detection and Classification Using Convolutional Neural Network Model. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113723] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
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31
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A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. MATHEMATICS 2020. [DOI: 10.3390/math8040555] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.
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32
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Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.015] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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34
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Al-jaboriy SS, Sjarif NNA, Chuprat S, Abduallah WM. Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.03.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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