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Siddiqi R, Javaid S. Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey. J Imaging 2024; 10:176. [PMID: 39194965 DOI: 10.3390/jimaging10080176] [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: 06/11/2024] [Revised: 07/15/2024] [Accepted: 07/19/2024] [Indexed: 08/29/2024] Open
Abstract
This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020-2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.
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Affiliation(s)
- Raheel Siddiqi
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
| | - Sameena Javaid
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
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2
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Shayegan MJ. A brief review and scientometric analysis on ensemble learning methods for handling COVID-19. Heliyon 2024; 10:e26694. [PMID: 38420425 PMCID: PMC10901105 DOI: 10.1016/j.heliyon.2024.e26694] [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: 12/22/2022] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.
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3
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Chen S, Ren S, Wang G, Huang M, Xue C. Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia From Chest X-Ray Images. IEEE J Biomed Health Inform 2024; 28:753-764. [PMID: 37027681 DOI: 10.1109/jbhi.2023.3247949] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.
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Ghnemat R, Alodibat S, Abu Al-Haija Q. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification. J Imaging 2023; 9:177. [PMID: 37754941 PMCID: PMC10532018 DOI: 10.3390/jimaging9090177] [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: 05/06/2023] [Revised: 08/19/2023] [Accepted: 08/23/2023] [Indexed: 09/28/2023] Open
Abstract
Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.
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Affiliation(s)
- Rawan Ghnemat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Sawsan Alodibat
- Department of Computer Science, Princess Sumaya University for Technology, Amman 11941, Jordan
| | - Qasem Abu Al-Haija
- Department of Cybersecurity, Princess Sumaya University for Technology, Amman 11941, Jordan
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5
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Panjeta M, Reddy A, Shah R, Shah J. Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-28. [PMID: 37362659 PMCID: PMC10224655 DOI: 10.1007/s11042-023-15247-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 03/10/2023] [Accepted: 03/30/2023] [Indexed: 06/28/2023]
Abstract
Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods.
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Affiliation(s)
- Manisha Panjeta
- Department of Computer Science and Engineering, Thapar Institute of Engineering Technology, Punjab, 147004 India
| | - Aryan Reddy
- Computer Science Department, NMIMS University, Mumbai, India
| | - Rushabh Shah
- Computer Science Department, NMIMS University, Mumbai, India
| | - Jash Shah
- Computer Science Department, NMIMS University, Mumbai, India
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Esmi N, Golshan Y, Asadi S, Shahbahrami A, Gaydadjiev G. A fuzzy fine-tuned model for COVID-19 diagnosis. Comput Biol Med 2023; 153:106483. [PMID: 36621192 PMCID: PMC9811914 DOI: 10.1016/j.compbiomed.2022.106483] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/16/2022] [Accepted: 12/25/2022] [Indexed: 01/06/2023]
Abstract
The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.
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Affiliation(s)
- Nima Esmi
- Faculty of Science and Engineering, University of Groningen, Netherlands.
| | - Yasaman Golshan
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Sara Asadi
- Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Asadollah Shahbahrami
- Faculty of Science and Engineering, University of Groningen, Netherlands; Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran.
| | - Georgi Gaydadjiev
- Faculty of Science and Engineering, University of Groningen, Netherlands.
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A Supervised Fuzzy Measure Learning Algorithm for Combining Classifiers. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Tang C, Hu C, Sun J, Wang SH, Zhang YD. NSCGCN: A novel deep GCN model to diagnosis COVID-19. Comput Biol Med 2022; 150:106151. [PMID: 36244303 PMCID: PMC9559311 DOI: 10.1016/j.compbiomed.2022.106151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 09/05/2022] [Accepted: 09/24/2022] [Indexed: 11/03/2022]
Abstract
AIM Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment. OBJECTIVES Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images. METHODS We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network). RESULTS Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%. CONCLUSION Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN). The source code of this work will be publicly available at https://github.com/TangChaosheng/NSCGCN.
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Affiliation(s)
- Chaosheng Tang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Chaochao Hu
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Junding Sun
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China.
| | - Shui-Hua Wang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
| | - Yu-Dong Zhang
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, PR China; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK; Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
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Mittal S, Venugopal VK, Agarwal VK, Malhotra M, Chatha JS, Kapur S, Gupta A, Batra V, Majumdar P, Malhotra A, Thakral K, Chhabra S, Vatsa M, Singh R, Chaudhury S. A novel abnormality annotation database for COVID-19 affected frontal lung X-rays. PLoS One 2022; 17:e0271931. [PMID: 36240175 PMCID: PMC9565456 DOI: 10.1371/journal.pone.0271931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 07/10/2022] [Indexed: 12/23/2022] Open
Abstract
Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.
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Affiliation(s)
- Surbhi Mittal
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | | | | | | | | | | | | | | | - Puspita Majumdar
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Aakarsh Malhotra
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Kartik Thakral
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | - Saheb Chhabra
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- Department of Computer Science, IIIT Delhi, New Delhi, India
| | - Mayank Vatsa
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
| | - Richa Singh
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
- * E-mail:
| | - Santanu Chaudhury
- Department of Computer Science, IIT Jodhpur, Karwar, Rajasthan, India
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Liu S, Cai T, Tang X, Zhang Y, Wang C. COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention. Comput Biol Med 2022; 149:106065. [PMID: 36081225 PMCID: PMC9433340 DOI: 10.1016/j.compbiomed.2022.106065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/07/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022]
Abstract
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.
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Affiliation(s)
- Shangwang Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China.
| | - Tongbo Cai
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Xiufang Tang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Yangyang Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
| | - Changgeng Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China; Engineering Lab of Intelligence Business & Internet of Things, Henan Province, China
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11
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Agbley BLY, Li J, Hossin MA, Nneji GU, Jackson J, Monday HN, James EC. Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images. Diagnostics (Basel) 2022; 12:diagnostics12071669. [PMID: 35885573 PMCID: PMC9323034 DOI: 10.3390/diagnostics12071669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients’ data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature.
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Affiliation(s)
- Bless Lord Y. Agbley
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Jianping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
- Correspondence:
| | - Md Altab Hossin
- School of Innovation and Entrepreneurship, Chengdu University, Chengdu 610106, China;
| | - Grace Ugochi Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Jehoiada Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
| | - Happy Nkanta Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (B.L.Y.A.); (H.N.M.)
| | - Edidiong Christopher James
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (E.C.J.)
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12
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Banerjee A, Roy S, Kundu R, Singh PK, Bhateja V, Sarkar R. An ensemble approach for still image-based human action recognition. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07514-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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13
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Basu A, Sheikh KH, Cuevas E, Sarkar R. COVID-19 detection from CT scans using a two-stage framework. EXPERT SYSTEMS WITH APPLICATIONS 2022; 193:116377. [PMID: 35002099 PMCID: PMC8720180 DOI: 10.1016/j.eswa.2021.116377] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 11/09/2021] [Accepted: 12/04/2021] [Indexed: 05/04/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive β -Hill Climbing (A β HC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at: https://github.com/khalid0007/Metaheuristic-Algorithms.
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Affiliation(s)
- Arpan Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Khalid Hassan Sheikh
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Erik Cuevas
- Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
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