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Liu X. The educational resource management based on image data visualization and deep learning. Heliyon 2024; 10:e32972. [PMID: 39040365 PMCID: PMC11261072 DOI: 10.1016/j.heliyon.2024.e32972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/24/2024] Open
Abstract
In order to address issues such as inaccurate education resource positioning and inefficient resource utilization, this study optimizes the Educational Resource Management System (ERMS) by combining image data visualization techniques with convolutional neural networks (CNNs) technology in deep learning. Firstly, the crucial role of ERMS in education and teaching is analyzed. Secondly, the application of image data visualization techniques and CNNs in the system is explained, along with the associated challenges. Finally, by optimizing the CNNs model and system architecture and validating with experimental data, the rationality of the proposed model is confirmed. Experimental results indicate a significant improvement in various performance metrics compared to traditional models. The recognition accuracy on the Mnist dataset reaches 98.1 %, and notably, on the cifar-10 dataset, the optimized model achieves an accuracy close to 98.3 % with improved runtime reduced to only 640.4 s. Additionally, through systematic simulation experiments, the designed system is shown to fully meet the earlier requirements for system functionality, validating the feasibility and rationality of the model and system in this study. Therefore, this study holds high practical value for optimizing ERMS and provides meaningful insights into image data visualization techniques and CNNs optimization.
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Affiliation(s)
- Xudong Liu
- University of the Cordilleras, Baguio City, 2600, Philippines
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2
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Lu G, Tian R, Yang W, Liu R, Liu D, Xiang Z, Zhang G. Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours. Front Med (Lausanne) 2024; 11:1402967. [PMID: 39036101 PMCID: PMC11257849 DOI: 10.3389/fmed.2024.1402967] [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: 03/18/2024] [Accepted: 06/14/2024] [Indexed: 07/23/2024] Open
Abstract
Objectives This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours. Methods Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion. We tested the performance of seven classifiers, namely, SVM, KNN, random forest, extra trees, XGBoost, LightGBM, and LR, on different feature models. Through feature fusion using ensemble and stacking strategies, we obtained the optimal classification model for benign and malignant breast tumours. Results In terms of traditional radiomics, the ensemble fusion strategy achieved the highest accuracy, AUC, and specificity, with values of 0.892, 0.942 [0.886-0.996], and 0.956 [0.873-1.000], respectively. The early fusion strategy with US, MG, and MRI achieved the highest sensitivity of 0.952 [0.887-1.000]. In terms of deep learning radiomics, the stacking fusion strategy achieved the highest accuracy, AUC, and sensitivity, with values of 0.937, 0.947 [0.887-1.000], and 1.000 [0.999-1.000], respectively. The early fusion strategies of US+MRI and US+MG achieved the highest specificity of 0.954 [0.867-1.000]. In terms of feature fusion, the ensemble and stacking approaches of the late fusion strategy achieved the highest accuracy of 0.968. In addition, stacking achieved the highest AUC and specificity, which were 0.997 [0.990-1.000] and 1.000 [0.999-1.000], respectively. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity of 1.000 [0.999-1.000] under the early fusion strategy. Conclusion This study demonstrated the potential of integrating deep learning and radiomic features with multimodal images. As a single modality, MRI based on radiomic features achieved greater accuracy than US or MG. The US and MG models achieved higher accuracy with transfer learning than the single-mode or radiomic models. The traditional radiomic and depth features of US+MG + MR achieved the highest sensitivity under the early fusion strategy, showed higher diagnostic performance, and provided more valuable information for differentiation between benign and malignant breast tumours.
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Affiliation(s)
- Guoxiu Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
| | - Ronghui Tian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Wei Yang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
| | - Ruibo Liu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Dongmei Liu
- Department of Ultrasound, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Zijie Xiang
- Biomedical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China
| | - Guoxu Zhang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
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3
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Ma W, Li M, Chu Z, Chen H. Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning. SENSORS (BASEL, SWITZERLAND) 2024; 24:3289. [PMID: 38894082 PMCID: PMC11174864 DOI: 10.3390/s24113289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/19/2024] [Indexed: 06/21/2024]
Abstract
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.
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Affiliation(s)
- Wenming Ma
- School of Computer and Control Engineering, Yantai University, Yantai 264005, China; (M.L.); (Z.C.); (H.C.)
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Gurmessa DK, Jimma W. Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review. BMJ Health Care Inform 2024; 31:e100954. [PMID: 38307616 PMCID: PMC10840064 DOI: 10.1136/bmjhci-2023-100954] [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: 11/06/2023] [Accepted: 01/21/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer diagnosis have been evaluated, the existing ethical challenges, research gaps, the XAI used and the relation between the accuracy and explainability of algorithms. METHODS In this work, Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and diagram were used. Peer-reviewed articles and conference proceedings from PubMed, IEEE Explore, ScienceDirect, Scopus and Google Scholar databases were searched. There is no stated date limit to filter the papers. The papers were searched on 19 September 2023, using various combinations of the search terms 'breast cancer', 'explainable', 'interpretable', 'machine learning', 'artificial intelligence' and 'XAI'. Rayyan online platform detected duplicates, inclusion and exclusion of papers. RESULTS This study identified 14 primary studies employing XAI for breast cancer diagnosis from mammography and US images. Out of the selected 14 studies, only 1 research evaluated humans' confidence in using the XAI system-additionally, 92.86% of identified papers identified dataset and dataset-related issues as research gaps and future direction. The result showed that further research and evaluation are needed to determine the most effective XAI method for breast cancer. CONCLUSION XAI is not conceded to increase users' and doctors' trust in the system. For the real-world application, effective and systematic evaluation of its trustworthiness in this scenario is lacking. PROSPERO REGISTRATION NUMBER CRD42023458665.
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Affiliation(s)
- Daraje Kaba Gurmessa
- Department of Information Science, Jimma Institute of Technology, Jimma University, Jimma, Oromia, Ethiopia
- Computer Science, Mattu University, Mattu, Oromīya, Ethiopia
| | - Worku Jimma
- Department of Information Science, Jimma Institute of Technology, Jimma University, Jimma, Oromia, Ethiopia
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5
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El-Adawi E, Essa E, Handosa M, Elmougy S. Wireless body area sensor networks based human activity recognition using deep learning. Sci Rep 2024; 14:2702. [PMID: 38302545 PMCID: PMC10834495 DOI: 10.1038/s41598-024-53069-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024] Open
Abstract
In the healthcare sector, the health status and biological, and physical activity of the patient are monitored among different sensors that collect the required information about these activities using Wireless body area network (WBAN) architecture. Sensor-based human activity recognition (HAR), which offers remarkable qualities of ease and privacy, has drawn increasing attention from researchers with the growth of the Internet of Things (IoT) and wearable technology. Deep learning has the ability to extract high-dimensional information automatically, making end-to-end learning. The most significant obstacles to computer vision, particularly convolutional neural networks (CNNs), are the effect of the environment background, camera shielding, and other variables. This paper aims to propose and develop a new HAR system in WBAN dependence on the Gramian angular field (GAF) and DenseNet. Once the necessary signals are obtained, the input signals undergo pre-processing through artifact removal and median filtering. In the initial stage, the time series data captured by the sensors undergoes a conversion process, transforming it into 2-dimensional images by using the GAF algorithm. Then, DenseNet automatically makes the processes and integrates the data collected from diverse sensors. The experiment results show that the proposed method achieves the best outcomes in which it achieves 97.83% accuracy, 97.83% F-measure, and 97.64 Matthews correlation coefficient (MCC).
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Affiliation(s)
- Ehab El-Adawi
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - Ehab Essa
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
| | - Mohamed Handosa
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
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6
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Wu Y, Luo L, Li Y, Sun Y, Huang X, Zhou Y, Wang Y, Wang Y, Zeng D, Yun L. A comparative assessment of time-consuming and laborious diatom analysis:Brief experimentation with suggestion of automatic identification. Forensic Sci Int 2024; 355:111939. [PMID: 38246065 DOI: 10.1016/j.forsciint.2024.111939] [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: 04/20/2022] [Revised: 11/02/2023] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
Diatom testing is considered a useful method for providing supportive evidence for the diagnosis of drowning in forensic pathology. However, various factors remain controversial for recognizing diatoms, such as being time-consuming and laborious and influencing the consistency of the results. Given the absence of precise and well-defined studies on this subject, this study aimed to determine the relationship between the ability to identify diatoms and researchers with different technical backgrounds. A total of 55 samples from 18 cases, including water, lungs, liver, and kidneys, were treated using the microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM), which was used to compare diatom analyses among three groups of well-trained forensic pathologists (FPs), trained junior employees (JEs), and new trainees (TEs). In addition to achieving similar accuracy of positive findings from drowning cases, counting efficiency was evaluated based on taxonomy records and counting time after viewing more than 5500 diatom images. In contrast to the higher counting efficiency of the JE group than that of the TE group, we observed a statistically significant difference (p < 0.05) in the diatom classification between these two groups. Based on our experiments, an efficient analysis for automatically identifying and classifying diatoms is urgently required.
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Affiliation(s)
- Yuhang Wu
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Lisiteng Luo
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuyang Li
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuntao Sun
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Xinyu Huang
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China
| | - Yuchi Zhou
- Criminal Investigation Department of Sichuan Provincial Public Security Bureau, NO.36, Wenmiaohoujie Street, Qingyang district, Chengdu 610015, People's Republic of China
| | - Yi Wang
- Criminal Investigation Department of Sichuan Provincial Public Security Bureau, NO.36, Wenmiaohoujie Street, Qingyang district, Chengdu 610015, People's Republic of China
| | - Yongqing Wang
- Criminal Science and Technology Division, Criminal Investigation Bureau, Chengdu Public Security Bureau, Chengdu, Sichuan 610031, People's Republic of China
| | - Debing Zeng
- Criminal Science and Technology Division, Criminal Investigation Bureau, Chengdu Public Security Bureau, Chengdu, Sichuan 610031, People's Republic of China
| | - Libing Yun
- Department of Forensic Pathology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, People' s Republic of China.
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7
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Basaad A, Basurra S, Vakaj E, Aleskandarany M, Abdelsamea MM. GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence. IEEE ACCESS 2024; 12:93993-94007. [DOI: 10.1109/access.2024.3424526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2024]
Affiliation(s)
- Abdullah Basaad
- School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K
| | - Shadi Basurra
- School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K
| | - Edlira Vakaj
- School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K
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Hou S, Liu X, Cao C, Huang Y. Gait Quality Aware Network: Toward the Interpretability of Silhouette-Based Gait Recognition. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:8978-8988. [PMID: 35294358 DOI: 10.1109/tnnls.2022.3154723] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gait recognition receives increasing attention since it can be conducted at a long distance in a nonintrusive way and applied to the condition of changing clothes. Most existing methods take the silhouettes of gait sequences as the input and learn a unified representation from multiple silhouettes to match probe and gallery. However, these models are all faced with the lack of interpretability, e.g., it is not clear which silhouette in a gait sequence and which part in the human body are relatively more important for recognition. In this work, we propose a gait quality aware network (GQAN) for gait recognition which explicitly assesses the quality of each silhouette and each part via two blocks: frame quality block (FQBlock) and part quality block (PQBlock). Specifically, FQBlock works in a squeeze-and-excitation style to recalibrate the features for each silhouette, and the scores of all the channels are added as frame quality indicator. PQBlock predicts a score for each part which is used to compute the weighted distance between the probe and gallery. Particularly, we propose a part quality loss (PQLoss) which enables GQAN to be trained in an end-to-end manner with only sequence-level identity annotations. This work is meaningful by moving toward the interpretability of silhouette-based gait recognition, and our method also achieves very competitive performance on CASIA-B and OUMVLP.
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9
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Zhang K, Chen R, Peng Z, Zhu Y, Wang X. FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:8338. [PMID: 37837167 PMCID: PMC10575317 DOI: 10.3390/s23198338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/24/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
In interpreting a scene for numerous applications, including autonomous driving and robotic navigation, semantic segmentation is crucial. Compared to single-modal data, multi-modal data allow us to extract a richer set of features, which is the benefit of improving segmentation accuracy and effect. We propose a point cloud semantic segmentation method, and a fusion graph convolutional network (FGCN) which extracts the semantic information of each point involved in the two-modal data of images and point clouds. The two-channel k-nearest neighbors (KNN) module of the FGCN was created to address the issue of the feature extraction's poor efficiency by utilizing picture data. Notably, the FGCN utilizes the spatial attention mechanism to better distinguish more important features and fuses multi-scale features to enhance the generalization capability of the network and increase the accuracy of the semantic segmentation. In the experiment, a self-made semantic segmentation KITTI (SSKIT) dataset was made for the fusion effect. The mean intersection over union (MIoU) of the SSKIT can reach 88.06%. As well as the public datasets, the S3DIS showed that our method can enhance data features and outperform other methods: the MIoU of the S3DIS can reach up to 78.55%. The segmentation accuracy is significantly improved compared with the existing methods, which verifies the effectiveness of the improved algorithms.
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Affiliation(s)
- Kun Zhang
- College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; (K.Z.); (R.C.); (Y.Z.)
| | - Rui Chen
- College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; (K.Z.); (R.C.); (Y.Z.)
| | - Zidong Peng
- College of International Education, Guangxi University of Science and Technology, Liuzhou 545006, China
| | - Yawei Zhu
- College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; (K.Z.); (R.C.); (Y.Z.)
| | - Xiaohong Wang
- College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China; (K.Z.); (R.C.); (Y.Z.)
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10
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Sahu A, Das PK, Meher S. Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms. Phys Med 2023; 114:103138. [PMID: 37914431 DOI: 10.1016/j.ejmp.2023.103138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 07/22/2023] [Accepted: 09/14/2023] [Indexed: 11/03/2023] Open
Abstract
OBJECTIVE Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems. METHODS We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned. RESULTS After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques. SIGNIFICANCE AND CONCLUSION In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
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Affiliation(s)
- Adyasha Sahu
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
| | - Pradeep Kumar Das
- School of Electronics Engineering (SENSE), VIT Vellore, Tamil Nadu, 632014, India.
| | - Sukadev Meher
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India.
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11
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Deb SD, Jha RK. Breast UltraSound Image classification using fuzzy-rank-based ensemble network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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12
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Majji R, G OPP, Rajeswari R, R C. Smart IoT in Breast Cancer Detection Using Optimal Deep Learning. J Digit Imaging 2023; 36:1489-1506. [PMID: 37221422 PMCID: PMC10406774 DOI: 10.1007/s10278-023-00834-9] [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: 04/02/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 05/25/2023] Open
Abstract
IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.
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Affiliation(s)
- Ramachandro Majji
- Department of Information Technology, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India.
| | - Om Prakash P G
- Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattangalathur, Chennai, Tamil Nadu, India
| | - R Rajeswari
- Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India
| | - Cristin R
- Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh, India
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Mukhtar R, Chang CY, Raja MAZ, Chaudhary NI. Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson's Disease Model. Biomimetics (Basel) 2023; 8:322. [PMID: 37504210 PMCID: PMC10807396 DOI: 10.3390/biomimetics8030322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023] Open
Abstract
The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson's disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg-Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.
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Affiliation(s)
- Roshana Mukhtar
- Department of Computer Science and Information Engineering, Graduate School of Engineering, Science and Technology, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
| | - Muhammad Asif Zahoor Raja
- Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
| | - Naveed Ishtiaq Chaudhary
- Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan;
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Cruz-Ramos C, García-Avila O, Almaraz-Damian JA, Ponomaryov V, Reyes-Reyes R, Sadovnychiy S. Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features. ENTROPY (BASEL, SWITZERLAND) 2023; 25:991. [PMID: 37509938 PMCID: PMC10378567 DOI: 10.3390/e25070991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Breast cancer is a disease that affects women in different countries around the world. The real cause of breast cancer is particularly challenging to determine, and early detection of the disease is necessary for reducing the death rate, due to the high risks associated with breast cancer. Treatment in the early period can increase the life expectancy and quality of life for women. CAD (Computer Aided Diagnostic) systems can perform the diagnosis of the benign and malignant lesions of breast cancer using technologies and tools based on image processing, helping specialist doctors to obtain a more precise point of view with fewer processes when making their diagnosis by giving a second opinion. This study presents a novel CAD system for automated breast cancer diagnosis. The proposed method consists of different stages. In the preprocessing stage, an image is segmented, and a mask of a lesion is obtained; during the next stage, the extraction of the deep learning features is performed by a CNN-specifically, DenseNet 201. Additionally, handcrafted features (Histogram of Oriented Gradients (HOG)-based, ULBP-based, perimeter area, area, eccentricity, and circularity) are obtained from an image. The designed hybrid system uses CNN architecture for extracting deep learning features, along with traditional methods which perform several handcraft features, following the medical properties of the disease with the purpose of later fusion via proposed statistical criteria. During the fusion stage, where deep learning and handcrafted features are analyzed, the genetic algorithms as well as mutual information selection algorithm, followed by several classifiers (XGBoost, AdaBoost, Multilayer perceptron (MLP)) based on stochastic measures, are applied to choose the most sensible information group among the features. In the experimental validation of two modalities of the CAD design, which performed two types of medical studies-mammography (MG) and ultrasound (US)-the databases mini-DDSM (Digital Database for Screening Mammography) and BUSI (Breast Ultrasound Images Dataset) were used. Novel CAD systems were evaluated and compared with recent state-of-the-art systems, demonstrating better performance in commonly used criteria, obtaining ACC of 97.6%, PRE of 98%, Recall of 98%, F1-Score of 98%, and IBA of 95% for the abovementioned datasets.
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Affiliation(s)
- Clara Cruz-Ramos
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Oscar García-Avila
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Jose-Agustin Almaraz-Damian
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Volodymyr Ponomaryov
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Rogelio Reyes-Reyes
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Sergiy Sadovnychiy
- Instituto Mexicano del Petroleo, Lazaro Cardenas Ave. # 152, Mexico City 07730, Mexico
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Alhussan AA, Abdelhamid AA, Towfek SK, Ibrahim A, Abualigah L, Khodadadi N, Khafaga DS, Al-Otaibi S, Ahmed AE. Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization. Biomimetics (Basel) 2023; 8:270. [PMID: 37504158 PMCID: PMC10377265 DOI: 10.3390/biomimetics8030270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/21/2023] [Accepted: 06/24/2023] [Indexed: 07/29/2023] Open
Abstract
Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.
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Affiliation(s)
- Amel Ali Alhussan
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Abdelaziz A Abdelhamid
- Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
| | - S K Towfek
- Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
- Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
| | - Abdelhameed Ibrahim
- Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
| | - Laith Abualigah
- Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
- MEU Research Unit, Middle East University, Amman 11831, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
| | - Nima Khodadadi
- Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
| | - Doaa Sami Khafaga
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Shaha Al-Otaibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ayman Em Ahmed
- Faculty of Engineering, King Salman International University, El-Tor 8701301, Egypt
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16
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To T, Lu T, Jorns JM, Patton M, Schmidt TG, Yen T, Yu B, Ye DH. Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer. Front Oncol 2023; 13:1179025. [PMID: 37397361 PMCID: PMC10313133 DOI: 10.3389/fonc.2023.1179025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/22/2023] [Indexed: 07/04/2023] Open
Abstract
Background Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method. Methods Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values. Results The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue. Conclusion The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.
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Affiliation(s)
- Tyrell To
- Department of Electrical and Computer Engineering, Marquette University, Opus College of Engineering, Milwaukee, WI, United States
| | - Tongtong Lu
- Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, United States
| | - Julie M. Jorns
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Mollie Patton
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Taly Gilat Schmidt
- Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tina Yen
- Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Bing Yu
- Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, United States
| | - Dong Hye Ye
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
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17
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Zhong Y, Peng Y, Lin Y, Chen D, Zhang H, Zheng W, Chen Y, Wu C. MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model. BMC Med Inform Decis Mak 2023; 23:82. [PMID: 37147619 PMCID: PMC10161645 DOI: 10.1186/s12911-023-02173-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/11/2023] [Indexed: 05/07/2023] Open
Abstract
BACKGROUND Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration. RESULTS We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification. CONCLUSIONS Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.
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Affiliation(s)
- Yating Zhong
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Yuzhong Peng
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China.
| | - Yanmei Lin
- School of Environment and Life Science, Nanning Normal University, Nanning, 530001, China.
| | - Dingjia Chen
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Hao Zhang
- School of Computer Science, Fudan University, Shanghai, 200433, China
- School of Computer, Guangdong University of Petrochemical Technology, Maoming, 525000, China
| | - Wen Zheng
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Yuanyuan Chen
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, China
| | - Changliang Wu
- Department of Spleen, Stomach and Liver Diseases, Guangxi International Zhuang Medical Hospital, Nanning, 530201, China
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18
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Razali NF, Isa IS, Sulaiman SN, A. Karim NK, Osman MK. CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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19
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Lin X, Chen J, Ma W, Tang W, Wang Y. EEG emotion recognition using improved graph neural network with channel selection. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107380. [PMID: 36745954 DOI: 10.1016/j.cmpb.2023.107380] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/11/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with promising applications in healthcare areas such as autism research and emotion detection in pregnant women. However, the complex data acquisition environment provides a variable number of EEG channels, which interferes with the model to simulate the process of information transfer in the human brain. Therefore, this paper proposes an improved graph convolution model with dynamic channel selection. METHODS The proposed model combines the advantages of 1D convolution and graph convolution to capture the intra- and inter-channel EEG features, respectively. We add functional connectivity in the graph structure that helps to simulate the relationship between brain regions further. In addition, an adjustable scale of channel selection can be performed based on the attention distribution in the graph structure. RESULTS We conducted various experiments on the DEAP-Twente, DEAP-Geneva, and SEED datasets and achieved average accuracies of 90.74%, 91%, and 90.22%, respectively, which exceeded most existing models. Meanwhile, with only 20% of the EEG channels retained, the models achieved average accuracies of 82.78%, 84%, and 83.93% on the above three datasets, respectively. CONCLUSIONS The experimental results show that the proposed model can achieve effective emotion classification in complex dataset environments. Also, the proposed channel selection method is informative for reducing the cost of affective computing.
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Affiliation(s)
- Xuefen Lin
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Jielin Chen
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China.
| | - Weifeng Ma
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Wei Tang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
| | - Yuchen Wang
- School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China
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20
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Zhu Z, Wang SH, Zhang YD. A Survey of Convolutional Neural Network in Breast Cancer. COMPUTER MODELING IN ENGINEERING & SCIENCES : CMES 2023; 136:2127-2172. [PMID: 37152661 PMCID: PMC7614504 DOI: 10.32604/cmes.2023.025484] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/28/2022] [Indexed: 05/09/2023]
Abstract
Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.
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Affiliation(s)
| | | | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
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21
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Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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22
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Zhu D, Yang W, Xu D, Li H, Zhao Y, Li D. A deep learning based two-layer predictor to identify enhancers and their strength. Methods 2023; 211:23-30. [PMID: 36740001 DOI: 10.1016/j.ymeth.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/03/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.
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Affiliation(s)
- Di Zhu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Dali Xu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
| | - Dan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
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23
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Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8342104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.
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Alsubai S, Alqahtani A, Sha M. Genetic hyperparameter optimization with Modified Scalable-Neighbourhood Component Analysis for breast cancer prognostication. Neural Netw 2023; 162:240-257. [PMID: 36913821 DOI: 10.1016/j.neunet.2023.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 12/30/2022] [Accepted: 02/23/2023] [Indexed: 03/02/2023]
Abstract
Breast cancer is common among women resulting in mortality when left untreated. Early detection is vital so that suitable treatment could assist cancer from spreading further and save people's life. The traditional way of detection is a time-consuming process. With the evolvement of DM (Data Mining), the healthcare industry could be benefitted in predicting the disease as it permits the physicians to determine the significant attributes for diagnosis. Though, conventional techniques have used DM-based methods to identify breast cancer, they lacked in terms of prediction rate. Moreover, parametric-Softmax classifiers have been a general option by conventional works with fixed classes, particularly when huge labelled data are present during training. Nevertheless, this turns into an issue for open set cases where new classes are encountered along with few instances to learn a generalized parametric classifier. Thus, the present study aims to implement a non-parametric strategy by optimizing the embedding of a feature rather than parametric classifiers. This research utilizes Deep CNN (Deep Convolutional Neural Network) and Inception V3 for learning visual features which preserve neighbourhood outline in semantic space relying on NCA (Neighbourhood Component Analysis) criteria. Delimited by its bottleneck, the study proposes MS-NCA (Modified Scalable-Neighbourhood Component Analysis) that relies on a non-linear objective function to perform feature fusion by optimizing the distance-learning objective due to which it gains the capability of computing inner feature products without performing mapping which increases the scalability of MS-NCA. Finally, G-HPO (Genetic-Hyper-parameter Optimization) is proposed. In this case, the new stage in the algorithm simply denotes the enhancement in the length of chromosome bringing several hyperparameters into subsequent XGBoost, NB and RF models having numerous layers for identifying the normal and affected cases of breast cancer for which optimized hyper-parameter values of RF (Random Forest), NB (Naïve Bayes), and XGBoost (eXtreme Gradient Boosting) are determined. This process helps in improvising the classification rate which is confirmed through analytical results.
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Affiliation(s)
- Shtwai Alsubai
- College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia.
| | - Abdullah Alqahtani
- College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia.
| | - Mohemmed Sha
- College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia.
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Mustafa Khan M, ul Islam MS, Siddiqui AA, Qadri MT. Dual deterministic model based on deep neural network for the classification of pneumonia. INTELLIGENT DECISION TECHNOLOGIES 2023. [DOI: 10.3233/idt-220192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Pneumonia is a disease caused by the virus (flu, respiratory Syncytial Virus) or bacteria. It can be fatal if not diagnosed and treated at an early stage. Chest X-rays have been widely utilized to diagnose such abnormalities with high exactitude and are primarily responsible for the augment real-world diagnosis process. Poor availability of authentic data and yardstick-based approaches and studies complicates the comparison process and identifying the safest recognition method. In this paper, a Dual Deterministic Model (DD-M) is proposed based on a Deep Neural network that would identify Pneumonia from chest X-ray and distinguish the cause in case of either viral or bacterial infection at an efficiency equivalent of an active radiologist. To accomplish the automated task of the proposed algorithm, an automatic computer-aided system is necessary. The proposed algorithm incorporates deep learning techniques to understand radiographic imaging better. The results were evaluated after implementing the proposed algorithm where; it reveals various aspects of the chest infected with Pneumonia compared to the healthy individual with approximately 97.45% accuracy and distinguishes between the viral and bacterial infection with the efficiency of 88.41%. The proposed algorithm with an improved image dataset will help the doctors diagnose.
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26
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Elkorany AS, Elsharkawy ZF. Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance. Sci Rep 2023; 13:2663. [PMID: 36792720 PMCID: PMC9932150 DOI: 10.1038/s41598-023-29875-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/11/2023] [Indexed: 02/17/2023] Open
Abstract
Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be saved by its early discovery. Mammography is frequently used to diagnose BC. The classification of mammography region of interest (ROI) patches (i.e., normal, malignant, or benign) is the most crucial phase in this process since it helps medical professionals to identify BC. In this paper, a hybrid technique that carries out a quick and precise classification that is appropriate for the BC diagnosis system is proposed and tested. Three different Deep Learning (DL) Convolution Neural Network (CNN) models-namely, Inception-V3, ResNet50, and AlexNet-are used in the current study as feature extractors. To extract useful features from each CNN model, our suggested method uses the Term Variance (TV) feature selection algorithm. The TV-selected features from each CNN model are combined and a further selection is performed to obtain the most useful features which are sent later to the multiclass support vector machine (MSVM) classifier. The Mammographic Image Analysis Society (MIAS) image database was used to test the effectiveness of the suggested method for classification. The mammogram's ROI is retrieved, and image patches are assigned to it. Based on the results of testing several TV feature subsets, the 600-feature subset with the highest classification performance was discovered. Higher classification accuracy (CA) is attained when compared to previously published work. The average CA for 70% of training is 97.81%, for 80% of training, it is 98%, and for 90% of training, it reaches its optimal value. Finally, the ablation analysis is performed to emphasize the role of the proposed network's key parameters.
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Affiliation(s)
- Ahmed S. Elkorany
- grid.411775.10000 0004 0621 4712Department of Electronics and Electrical Comm. Eng., Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt
| | - Zeinab F. Elsharkawy
- grid.429648.50000 0000 9052 0245Engineering Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo, Egypt
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27
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High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Hao S, Huang C, Heidari AA, Xu Z, Chen H, Althobaiti MM, Mansour RF, Chen X. Performance optimization of water cycle algorithm for multilevel lupus nephritis image segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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29
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Modak S, Abdel-Raheem E, Rueda L. Applications of Deep Learning in Disease Diagnosis of Chest Radiographs: A Survey on Materials and Methods. BIOMEDICAL ENGINEERING ADVANCES 2023. [DOI: 10.1016/j.bea.2023.100076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
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30
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Wu Y, Wang X, Zhao W, Lv X. A Novel Topic Clustering Algorithm Based on Graph Neural Network for Question Topic Diversity. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
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31
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Su H, Zhao D, Yu F, Heidari AA, Xu Z, Alotaibi FS, Mafarja M, Chen H. A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems. JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023; 10:36-64. [DOI: 10.1093/jcde/qwac112] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2024]
Abstract
Abstract
As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.
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Affiliation(s)
- Hang Su
- College of Computer Science and Technology, Changchun Normal University , Changchun, Jilin 130032, China
| | - Dong Zhao
- College of Computer Science and Technology, Changchun Normal University , Changchun, Jilin 130032, China
| | - Fanhua Yu
- College of Computer Science and Technology, Changchun Normal University , Changchun, Jilin 130032, China
| | - Ali Asghar Heidari
- College of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou, Zhejiang 325035, China
| | - Zhangze Xu
- College of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou, Zhejiang 325035, China
| | - Fahd S Alotaibi
- Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah 21589, Saudi Arabia
| | - Majdi Mafarja
- Department of Computer Science, Birzeit University , PO Box 14, Birzeit, West Bank, Palestine
- Faculty of Computing and Information Technology, King Abdulaziz University , Jeddah 21589, Saudi Arabia
| | - Huiling Chen
- College of Computer Science and Artificial Intelligence, Wenzhou University , Wenzhou, Zhejiang 325035, China
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Jayaram N, Muralidharan M, Muthupandian S. The use of multilayer perceptron and radial basis function: an artificial intelligence model to predict progression of oral cancer. Int J Surg 2023; 109:57-59. [PMID: 36799795 PMCID: PMC10389180 DOI: 10.1097/js9.0000000000000026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 02/18/2023]
Affiliation(s)
- Nivedita Jayaram
- Department of Computing Technologies, SRM Institute of Science and Technology
| | - Manjusha Muralidharan
- AMR and Nanomedicine Laboratory, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
| | - Saravanan Muthupandian
- AMR and Nanomedicine Laboratory, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, India
- Department of Medical Microbiology and Immunology, Institute of Biomedical Sciences, College of Health Sciences, Mekelle University, Mekelle, Ethiopia
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Sun J, Liu Q, Wang Y, Wang L, Song X, Zhao X. Five-year prognosis model of esophageal cancer based on genetic algorithm improved deep neural network. Ing Rech Biomed 2023. [DOI: 10.1016/j.irbm.2022.100748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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34
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Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:7717712. [PMID: 36909966 PMCID: PMC9998154 DOI: 10.1155/2023/7717712] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/15/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.
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Catteau X, Zindy E, Bouri S, Noël JC, Salmon I, Decaestecker C. Comparison Between Manual and Automated Assessment of Ki-67 in Breast Carcinoma: Test of a Simple Method in Daily Practice. Technol Cancer Res Treat 2023; 22:15330338231169603. [PMID: 37559526 PMCID: PMC10416654 DOI: 10.1177/15330338231169603] [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] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND In the era of "precision medicine," the availability of high-quality tumor biomarker tests is critical and tumor proliferation evaluated by Ki-67 antibody is one of the most important prognostic factors in breast cancer. But the evaluation of Ki-67 index has been shown to suffer from some interobserver variability. The goal of the study is to develop an easy, automated, and reliable Ki-67 assessment approach for invasive breast carcinoma in routine practice. PATIENTS AND METHODS A total of 151 biopsies of invasive breast carcinoma were analyzed. The Ki-67 index was evaluated by 2 pathologists with MIB-1 antibody as a global tumor index and also in a hotspot. These 2 areas were also analyzed by digital image analysis (DIA). RESULTS For Ki-67 index assessment, in the global and hotspot tumor area, the concordances were very good between DIA and pathologists when DIA focused on the annotations made by pathologist (0.73 and 0.83, respectively). However, this was definitely not the case when DIA was not constrained within the pathologist's annotations and automatically established its global or hotspot area in the whole tissue sample (concordance correlation coefficients between 0.28 and 0.58). CONCLUSIONS The DIA technique demonstrated a meaningful concordance with the indices evaluated by pathologists when the tumor area is previously identified by a pathologist. In contrast, basing Ki-67 assessment on automatic tissue detection was not satisfactory and provided bad concordance results. A representative tumoral zone must therefore be manually selected prior to the measurement made by the DIA.
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Affiliation(s)
- Xavier Catteau
- Department of Pathology, Erasme's Hospital, Université Libre de Bruxelles, Brussels, Belgium
- Curepath laboratory, CHU Tivoli and CHIREC institute, Jumet, Belgium
| | - Egor Zindy
- Laboratory of Image Synthesis and Analysis (LISA), Université Libre de Bruxelles, Bruxelles, Belgium
- Digital Pathology Platform of the CMMI (DIAPath), Université Libre de Bruxelles, Gosselies, Belgium
| | - Sarah Bouri
- Department of Pathology, Erasme's Hospital, Université Libre de Bruxelles, Brussels, Belgium
- Curepath laboratory, CHU Tivoli and CHIREC institute, Jumet, Belgium
| | - Jean-Christophe Noël
- Department of Pathology, Erasme's Hospital, Université Libre de Bruxelles, Brussels, Belgium
- Curepath laboratory, CHU Tivoli and CHIREC institute, Jumet, Belgium
| | - Isabelle Salmon
- Department of Pathology, Erasme's Hospital, Université Libre de Bruxelles, Brussels, Belgium
- Digital Pathology Platform of the CMMI (DIAPath), Université Libre de Bruxelles, Gosselies, Belgium
| | - Christine Decaestecker
- Laboratory of Image Synthesis and Analysis (LISA), Université Libre de Bruxelles, Bruxelles, Belgium
- Digital Pathology Platform of the CMMI (DIAPath), Université Libre de Bruxelles, Gosselies, Belgium
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Mangla C, Rani S, Herencsar N. A misbehavior detection framework for cooperative intelligent transport systems. ISA TRANSACTIONS 2023; 132:52-60. [PMID: 36154778 DOI: 10.1016/j.isatra.2022.08.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/31/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
With changing times, the need for security increases in all fields, whether we talk about cloud networks or vehicular networks. In every place, it has its importance, but in vehicular networks where the lives of human beings are involved, security becomes the topmost priority. Therefore, this article aims to shed light on Misbehavior Detection Framework (MDF) used in the Cooperative Intelligent Transport Systems community. Here, MDF keeps an eye on malicious entities on the roads. It is done by regularly evaluating two main checks: consistency and local plausibility. These checks are done by Intelligent Transport System Stations. All the messages received through Vehicle-to-Everything are scrutinized through this model. After that, all the messages are evaluated by local detection mechanisms to decide the holistic message's plausibility. This article mainly focuses on the logic behind the proposed Misbehavior Detection Framework providing more security, evaluating various Machine Learning-based models to ensure one best out of all based on quality and computation latency of all models along with the results of various parameters, such as Recall, Precision, F1 Score, Accuracy, Bookmaker Informedness, Markedness, Mathews Correlation Coefficient, Kappa, and achieved the best results.
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Affiliation(s)
- Cherry Mangla
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura-140401, Punjab, India.
| | - Shalli Rani
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura-140401, Punjab, India.
| | - Norbert Herencsar
- Department of Telecommunications, Faculty of Electrical Engineering and Communications, Brno University of Technology, Technicka 3082/12, 61600 Brno, Czech Republic.
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Xie X, Li Y, Gao Y, Wu C, Gao P, Song B, Wang W, Lu Y. Weakly supervised object localization with soft guidance and channel erasing for auto labelling in autonomous driving systems. ISA TRANSACTIONS 2023; 132:39-51. [PMID: 36075781 DOI: 10.1016/j.isatra.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/08/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
Automated driving systems (ADSs) conceive an efficient and safe way of driving. The safety of ADSs depends on a precise object detector that needs to be upgraded continuously facing various environments. Massive annotations are required to utilize collected images of surroundings through vehicles and accommodate new environments. Auto labelling is one approach to alleviate such dilemma. To this end, we propose a novel Weakly Supervised Object Localization (WSOL) method which can localize objects precisely without detection annotations. This paper proposed Soft Guidance Module (SGM), Channel Erasing Module (CEM) and incorporate them into a multi-flow framework allowing the two mutually beneficial. Finally, experiments and visualizations are performed to evaluate our method on Stanford Cars, ILSVRC 2016 and CUB-200-2011 datasets.
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Affiliation(s)
- Xinyan Xie
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Yijiang Li
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, Guangdong, China.
| | - Ying Gao
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Chaojie Wu
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Ping Gao
- Department of Geriatric Respiratory Medicine, Guangdong Provincial People's Hospital, Guangzhou, 510080, Guangdong, China.
| | - Binjie Song
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Wei Wang
- School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510006, Guangdong, China.
| | - Yiqin Lu
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.
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38
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Duong LT, Chu CQ, Nguyen PT, Nguyen ST, Tran BQ. Edge detection and graph neural networks to classify mammograms: A case study with a dataset from Vietnamese patients. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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39
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Wang Y, Jin Y, Li M, Zhang J, Wang S, Zhang H, Song B. Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis. Medicine (Baltimore) 2022; 101:e31574. [PMID: 36343063 PMCID: PMC9646663 DOI: 10.1097/md.0000000000031574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 10/06/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer. METHODS A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer. RESULTS Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed. CONCLUSIONS Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.
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Affiliation(s)
- Yewu Wang
- Department of Joint and Sports Medicine, Qujing First People’s Hospital, Qujing, Yunan Province, China
| | - Yumei Jin
- Department of Medical Imaging Center, Qujing First People’s Hospital, Qujing, Yunan Province, China
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Mou Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Jun Zhang
- Department of Radiology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Shaoyu Wang
- Siemens Medical System Co., LTD, Magnetic Resonance Imaging Research Department, Shanghai, China
| | - Huapeng Zhang
- Siemens Medical System Co., LTD, Magnetic Resonance Imaging Research Department, Shanghai, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
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Yang X, Xi X, Yang L, Xu C, Song Z, Nie X, Qiao L, Li C, Shi Q, Yin Y. Multi-modality relation attention network for breast tumor classification. Comput Biol Med 2022; 150:106210. [PMID: 37859295 DOI: 10.1016/j.compbiomed.2022.106210] [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: 02/18/2022] [Revised: 09/05/2022] [Accepted: 10/09/2022] [Indexed: 11/03/2022]
Abstract
Automatic breast image classification plays an important role in breast cancer diagnosis, and multi-modality image fusion may improve classification performance. However, existing fusion methods ignore relevant multi-modality information in favor of improving the discriminative ability of single-modality features. To improve classification performance, this paper proposes a multi-modality relation attention network with consistent regularization for breast tumor classification using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Within the proposed network, a novel multi-modality relation attention module improves the discriminative ability of single-modality features by exploring the correlation information between two modalities. In addition, a module ensures the classification consistency of ADC and DWI modality, thus improving robustness to noise. Experimental results on our database demonstrate that the proposed method is effective for breast tumor classification, and outperforms existing multi-modality fusion methods. The AUC, accuracy, specificity, and sensitivity are 85.1%, 86.7%, 83.3%, and 88.9% respectively.
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Affiliation(s)
- Xiao Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China.
| | - Lu Yang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Chuanzhen Xu
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Zuoyong Song
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Xiushan Nie
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Lishan Qiao
- School of Mathematical Sciences, Liaocheng University, Liaocheng, 252000, China
| | - Chenglong Li
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | - Qinglei Shi
- Diagnosis Imaging, Siemens Healthcare Ltd, Beijing, 100102, China
| | - Yilong Yin
- School of Software, Shandong University, Jinan, 250101, China
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41
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Heterogeneous Question Answering Community Detection Based on Graph Neural Network. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.10.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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42
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Breast cancer image analysis using deep learning techniques – a survey. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00703-5] [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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43
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Bi S, Li Z, Brown M, Wang L, Xu Y. Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm. ICST TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2022. [DOI: 10.4108/eetsis.v9i5.1567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm.
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Subha Darathy C, Agees Kumar C. A novel deep neural network with adaptive sine cosine crow search (DNN-ASCCS) model for content based medical image reterival. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-222872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Tumor is the second major cause of death in women worldwide. Breast cancer diagnosis and treatment can be difficult for radiologists. As a result, primary care helps to avoid disease and mortality. The study’s main goal is to improve treatment choices and to save lives by detecting breast cancer earlier. For classification problems, we propose a DNN-ASCC architecture in this study. The Fast Non-Local Means Filter completes the initial preprocessing stage. The binary grasshopper optimization algorithm (BGOA) and the grey-level run length matrix are utilized to choose the best features for the feature extraction operation. The suggested hybrid classifier (DNN-ASCCS) is critical for identifying normal and malignant tumors. Breast cancer is accurately detected by the suggested hybrid classifier. The recommended (DNN-ASCCS) was developed using MATLAB and datasets from the BIDCIDRI. The results of the simulation showed that the proposed technique has an accurate results in classification (99.17 percent) and robustness analysis is also done. When compared to alternative approaches, experimental results show that the suggested method is efficient.
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Affiliation(s)
- C. Subha Darathy
- Department of CSE, Arunachala College of Engineering for Women, Vellichanthai
| | - C. Agees Kumar
- Department of EEE, Arunachala College of Engineering for Women, Vellichanthai
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45
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Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare (Basel) 2022; 10:healthcare10101892. [PMID: 36292339 PMCID: PMC9602147 DOI: 10.3390/healthcare10101892] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 12/04/2022] Open
Abstract
Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.
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46
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Hybrid CNN-LSTM and modified wild horse herd Model-based prediction of genome sequences for genetic disorders. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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47
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Clustering-Based Fusion for Medical Information Retrieval. J Biomed Inform 2022; 135:104213. [DOI: 10.1016/j.jbi.2022.104213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 09/17/2022] [Accepted: 09/24/2022] [Indexed: 10/31/2022]
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48
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Cheng Y, Wang A, Wu L. A Classification Method for Electronic Components Based on Siamese Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:6478. [PMID: 36080937 PMCID: PMC9460278 DOI: 10.3390/s22176478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/18/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
In the field of electronics manufacturing, electronic component classification facilitates the management and recycling of the functional and valuable electronic components in electronic waste. Current electronic component classification methods are mainly based on deep learning, which requires a large number of samples to train the model. Owing to the wide variety of electronic components, collecting datasets is a time-consuming and laborious process. This study proposed a Siamese network-based classification method to solve the electronic component classification problem for a few samples. First, an improved visual geometry group 16 (VGG-16) model was proposed as the feature extraction part of the Siamese neural network to improve the recognition performance of the model under small samples. Then, a novel channel correlation loss function that allows the model to learn the correlation between different channels in the feature map was designed to further improve the generalization performance of the model. Finally, the nearest neighbor algorithm was used to complete the classification work. The experimental results show that the proposed method can achieve high classification accuracy under small sample conditions and is robust for electronic components with similar appearances. This improves the classification quality of electronic components and reduces the training sample collection cost.
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Affiliation(s)
| | - Aimin Wang
- Correspondence: ; Tel.: +86-135-2266-2896
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput Sci 2022; 8:e1054. [PMID: 36092017 PMCID: PMC9454783 DOI: 10.7717/peerj-cs.1054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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Cao H. Entrepreneurship education-infiltrated computer-aided instruction system for college Music Majors using convolutional neural network. Front Psychol 2022; 13:900195. [PMID: 35928420 PMCID: PMC9343719 DOI: 10.3389/fpsyg.2022.900195] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 07/01/2022] [Indexed: 11/18/2022] Open
Abstract
The purpose is to improve the teaching and learning efficiency of college Innovation and Entrepreneurship Education (IEE). Firstly, from the perspective of aesthetic education, this work designs the teacher and student sides of the Computer-aided Instruction (CAI) system. Secondly, the CAI model is implemented based on the weight sharing and local perception of the Convolutional Neural Network (CNN). Finally, the performance of the CNN-based CAI model is tested. Meanwhile, it analyses students’ IEE experience under the proposed CAI model through a case study of Music Majors from Xi’an Conservatory of Music. The experimental data show that the CNN-based CAI model can respond quickly and stably when users access different functional modules, such as webpage browsing. The proposed CAI model increases students’ entrepreneurial interest, skills, and knowledge by 55.62, 57.32, and 72.12%, respectively. Students’ entrepreneurial practice ability has been improved by over 50.00%; such an increase in entrepreneurial practice ability has also shown individual differences. Thus, the proposed Music Majors-oriented IEE-infiltrated CAI model based on CNN improves students’ entrepreneurial practice ability and reflects the positive experience of Music Majors on IEE. The finding provides references for the step-by-step identification of the CNN-based CAI model and has certain guiding significance for analyzing the effect of college IEE.
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