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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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Fu J, Peng H, Li B, Liu Z, Lugu R, Wang J, Ramírez-de-Arellano A. Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models. Int J Neural Syst 2024; 34:2450032. [PMID: 38624267 DOI: 10.1142/s0129065724500321] [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: 04/17/2024]
Abstract
Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.
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Affiliation(s)
- Jun Fu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Bing Li
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Zhicai Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Rikong Lugu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
| | - Antonio Ramírez-de-Arellano
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
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3
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Yue G, Yang C, Zhao Z, An Z, Yang Y. ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception. Front Physiol 2023; 14:1296185. [PMID: 38028767 PMCID: PMC10679680 DOI: 10.3389/fphys.2023.1296185] [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: 09/18/2023] [Accepted: 11/02/2023] [Indexed: 12/01/2023] Open
Abstract
The Segmentation of infected areas from COVID-19 chest X-ray (CXR) images is of great significance for the diagnosis and treatment of patients. However, accurately and effectively segmenting infected areas of CXR images is still challenging due to the inherent ambiguity of CXR images and the cross-scale variations in infected regions. To address these issues, this article proposes a ERGPNet based on embedded residuals and global perception, to segment lesion regions in COVID-19 CXR images. First, aiming at the inherent fuzziness of CXR images, an embedded residual convolution structure is proposed to enhance the ability of internal feature extraction. Second, a global information perception module is constructed to guide the network in generating long-distance information flow, alleviating the interferences of cross-scale variations on the algorithm's discrimination ability. Finally, the network's sensitivity to target regions is improved, and the interference of noise information is suppressed through the utilization of parallel spatial and serial channel attention modules. The interactions between each module fully establish the mapping relationship between feature representation and information decision-making and improve the accuracy of lesion segmentation. Extensive experiments on three datasets of COVID-19 CXR images, and the results demonstrate that the proposed method outperforms other state-of-the-art segmentation methods of CXR images.
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Affiliation(s)
- Gongtao Yue
- School of Computer Science, Xijing University, Xi’an, China
| | - Chen Yang
- School of Computer Science, Xijing University, Xi’an, China
| | - Zhengyang Zhao
- School of Information and Navigation, Air Force Engineering University, Xi’an, China
| | - Ziheng An
- School of Integrated Circuits, Anhui University, Hefei, China
| | - Yongsheng Yang
- School of Computer Science, Xijing University, Xi’an, China
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4
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Murphy K, Muhairwe J, Schalekamp S, van Ginneken B, Ayakaka I, Mashaete K, Katende B, van Heerden A, Bosman S, Madonsela T, Gonzalez Fernandez L, Signorell A, Bresser M, Reither K, Glass TR. COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests. Sci Rep 2023; 13:19692. [PMID: 37952026 PMCID: PMC10640556 DOI: 10.1038/s41598-023-46461-w] [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: 12/15/2022] [Accepted: 11/01/2023] [Indexed: 11/14/2023] Open
Abstract
Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.
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Affiliation(s)
- Keelin Murphy
- Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands.
| | | | - Steven Schalekamp
- Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Irene Ayakaka
- SolidarMed, Partnerships for Health, Maseru, Lesotho
| | | | | | - Alastair van Heerden
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
- SAMRC/WITS Developmental Pathways for Health Research Unit, Department of Paediatrics, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, Gauteng, South Africa
| | - Shannon Bosman
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Thandanani Madonsela
- Centre for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Lucia Gonzalez Fernandez
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland
- SolidarMed, Partnerships for Health, Lucerne, Switzerland
| | - Aita Signorell
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Moniek Bresser
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Klaus Reither
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Tracy R Glass
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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5
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Li H, Wu Y, Hu H, Lu H, Huang Q, Wan S. Interpretable thoracic pathologic prediction via learning group-disentangled representation. Methods 2023; 218:110-117. [PMID: 37543302 DOI: 10.1016/j.ymeth.2023.08.001] [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: 03/09/2023] [Revised: 05/21/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023] Open
Abstract
Deep learning has brought a significant progress in medical image analysis. However, their lack of interpretability might bring high risk for wrong diagnosis with limited clinical knowledge embedding. In other words, we believe it's crucial for humans to interpret how deep learning work for medical analysis, thus appropriately adding knowledge constraints to correct the bias of wrong results. With such purpose, we propose Representation Group-Disentangling Network (RGD-Net) to explain the process of feature extraction and decision making inside deep learning framework, where we completely disentangle feature space of input X-ray images into independent feature groups, and each group would contribute to diagnose of a specific disease. Specifically, we first state problem definition for interpretable prediction with auto-encoder structure. Then, group-disentangled representations are extracted from input X-ray images with the proposed Group-Disentangle Module, which constructs semantic latent space by enforcing semantic consistency of attributes. Afterwards, adversarial constricts on mapping from features to diseases are proposed to prevent model collapse during training. Finally, a novel design of local tuning medical application is proposed based on RGB-Net, which is capable to aid clinicians for reasonable diagnosis. By conducting quantity of experiments on public datasets, RGD-Net have been superior to comparative studies by leveraging potential factors contributing to different diseases. We believe our work could bring interpretability in digging inherent patterns of deep learning on medical image analysis.
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Affiliation(s)
- Hao Li
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210093, China; College of Computer and Information, Hohai University, Nanjing 210093, China.
| | - Yirui Wu
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210093, China; College of Computer and Information, Hohai University, Nanjing 210093, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China.
| | - Hexuan Hu
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210093, China; College of Computer and Information, Hohai University, Nanjing 210093, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China.
| | - Hu Lu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
| | - Qian Huang
- Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210093, China; College of Computer and Information, Hohai University, Nanjing 210093, China.
| | - Shaohua Wan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China.
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Hu D, Li X, Lin C, Wu Y, Jiang H. Deep Learning to Predict the Cell Proliferation and Prognosis of Non-Small Cell Lung Cancer Based on FDG-PET/CT Images. Diagnostics (Basel) 2023; 13:3107. [PMID: 37835850 PMCID: PMC10573026 DOI: 10.3390/diagnostics13193107] [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: 07/28/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan-Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC.
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Affiliation(s)
- Dehua Hu
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Xiang Li
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Chao Lin
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
| | - Yonggang Wu
- Department of Nuclear Medicine & PET Imaging Center, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Hao Jiang
- Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha 410013, China
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Rafique Q, Rehman A, Afghan MS, Ahmad HM, Zafar I, Fayyaz K, Ain Q, Rayan RA, Al-Aidarous KM, Rashid S, Mushtaq G, Sharma R. Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations. Comput Biol Med 2023; 163:107191. [PMID: 37354819 PMCID: PMC10281043 DOI: 10.1016/j.compbiomed.2023.107191] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/28/2023] [Accepted: 06/19/2023] [Indexed: 06/26/2023]
Abstract
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.
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Affiliation(s)
- Qandeel Rafique
- Department of Internal Medicine, Sahiwal Medical College, Sahiwal, 57040, Pakistan.
| | - Ali Rehman
- Department of General Medicine Govt. Eye and General Hospital Lahore, 54000, Pakistan.
| | - Muhammad Sher Afghan
- Department of Internal Medicine District Headquarter Hospital Faislaabad, 62300, Pakistan.
| | - Hafiz Muhamad Ahmad
- Department of Internal Medicine District Headquarter Hospital Bahawalnagar, 62300, Pakistan.
| | - Imran Zafar
- Department of Bioinformatics and Computational Biology, Virtual University Pakistan, 44000, Pakistan.
| | - Kompal Fayyaz
- Department of National Centre for Bioinformatics, Quaid-I-Azam University Islamabad, 45320, Pakistan.
| | - Quratul Ain
- Department of Chemistry, Government College Women University Faisalabad, 03822, Pakistan.
| | - Rehab A Rayan
- Department of Epidemiology, High Institute of Public Health, Alexandria University, 21526, Egypt.
| | - Khadija Mohammed Al-Aidarous
- Department of Computer Science, College of Science and Arts in Sharurah, Najran University, 51730, Saudi Arabia.
| | - Summya Rashid
- Department of Pharmacology & Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia.
| | - Gohar Mushtaq
- Center for Scientific Research, Faculty of Medicine, Idlib University, Idlib, Syria.
| | - Rohit Sharma
- Department of Rasashastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India.
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8
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Experimental analysis of machine learning methods to detect Covid-19 from x-rays. JOURNAL OF ENGINEERING RESEARCH 2023; 11:100063. [PMCID: PMC10065050 DOI: 10.1016/j.jer.2023.100063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 02/02/2024]
Abstract
To automate the detection of covid-19 patients most have proposed deep learning neural networks to classify patients using large databases of chest x-rays. Very few used classical machine learning methods. Machine learning methods may require less computational power and perform well if the data set is small. We experiment with classical machine learning methods on three different data sources varying in size from 55 to almost 4000 samples. We experiment with four feature extraction methods of Gabor, SURF, LBP, and HOG. Backpropagation neural networks and k-nearest neighbor classifiers are combined using one of the four combining methods of bagging, RSM, ARCx4 boosting and Ada-boosting. Results show that using the proper feature extraction and feature selection methods very high performance can be reached using simple backpropagation neural network classifiers. Regardless of combiner method used, the best classification rate achieved was 99.06% for the largest data set, and 100% for the smallest data set.
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Abir FF, Chowdhury MEH, Tapotee MI, Mushtak A, Khandakar A, Mahmud S, Hasan MA. PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106130. [PMID: 37006447 PMCID: PMC10047244 DOI: 10.1016/j.engappai.2023.106130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing.
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Affiliation(s)
- Farhan Fuad Abir
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
| | | | - Malisha Islam Tapotee
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Md Anwarul Hasan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
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Yuan J, Wu F, Li Y, Li J, Huang G, Huang Q. DPDH-CapNet: A Novel Lightweight Capsule Network with Non-routing for COVID-19 Diagnosis Using X-ray Images. J Digit Imaging 2023; 36:988-1000. [PMID: 36813978 PMCID: PMC9946284 DOI: 10.1007/s10278-023-00791-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/26/2023] [Accepted: 01/29/2023] [Indexed: 02/24/2023] Open
Abstract
COVID-19 has claimed millions of lives since its outbreak in December 2019, and the damage continues, so it is urgent to develop new technologies to aid its diagnosis. However, the state-of-the-art deep learning methods often rely on large-scale labeled data, limiting their clinical application in COVID-19 identification. Recently, capsule networks have achieved highly competitive performance for COVID-19 detection, but they require expensive routing computation or traditional matrix multiplication to deal with the capsule dimensional entanglement. A more lightweight capsule network is developed to effectively address these problems, namely DPDH-CapNet, which aims to enhance the technology of automated diagnosis for COVID-19 chest X-ray images. It adopts depthwise convolution (D), point convolution (P), and dilated convolution (D) to construct a new feature extractor, thus successfully capturing the local and global dependencies of COVID-19 pathological features. Simultaneously, it constructs the classification layer by homogeneous (H) vector capsules with an adaptive, non-iterative, and non-routing mechanism. We conduct experiments on two publicly available combined datasets, including normal, pneumonia, and COVID-19 images. With a limited number of samples, the parameters of the proposed model are reduced by 9x compared to the state-of-the-art capsule network. Moreover, our model has faster convergence speed and better generalization, and its accuracy, precision, recall, and F-measure are improved to 97.99%, 98.05%, 98.02%, and 98.03%, respectively. In addition, experimental results demonstrate that, contrary to the transfer learning method, the proposed model does not require pre-training and a large number of training samples.
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Affiliation(s)
- Jianjun Yuan
- College of Artificial Intelligence, Southwest University, Chongqing, 40075, China.
| | - Fujun Wu
- College of Artificial Intelligence, Southwest University, Chongqing, 40075, China
| | - Yuxi Li
- College of Artificial Intelligence, Southwest University, Chongqing, 40075, China
| | - Jinyi Li
- College of Artificial Intelligence, Southwest University, Chongqing, 40075, China
| | - Guojun Huang
- College of Artificial Intelligence, Southwest University, Chongqing, 40075, China
| | - Quanyong Huang
- College of Machinery and Automation, Wuhan University of Science and Technology, Heping Avenue No. 947, Wuhan, Hubei Province, 430091, China.
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11
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Cao C, Song J, Su R, Wu X, Wang Z, Hou M. Structure-constrained deep feature fusion for chronic otitis media and cholesteatoma identification. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-21. [PMID: 37362730 PMCID: PMC10157598 DOI: 10.1007/s11042-023-15425-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/19/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) were two most common chronic middle ear disease(MED) clinically. Accurate differential diagnosis between these two diseases is of high clinical importance given the difference in etiologies, lesion manifestations and treatments. The high-resolution computed tomography (CT) scanning of the temporal bone presents a better view of auditory structures, which is currently regarded as the first-line diagnostic imaging modality in the case of MED. In this paper, we first used a region-of-interest (ROI) network to find the area of the middle ear in the entire temporal bone CT image and segment it to a size of 100*100 pixels. Then, we used a structure-constrained deep feature fusion algorithm to convert different characteristic features of the middle ear in three groups as suppurative otitis media (CSOM), middle ear cholesteatoma (MEC) and normal patches. To fuse structure information, we introduced a graph isomorphism network that implements a feature vector from neighbourhoods and the coordinate distance between vertices. Finally, we construct a classifier named the "otitis media, cholesteatoma and normal identification classifier" (OMCNIC). The experimental results achieved by the graph isomorphism network revealed a 96.36% accuracy in all CSOM and MEC classifications. The experimental results indicate that our structure-constrained deep feature fusion algorithm can quickly and effectively classify CSOM and MEC. It will help otologist in the selection of the most appropriate treatment, and the complications can also be reduced.
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Affiliation(s)
- Cong Cao
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
| | - Jian Song
- Department of Otorhinolaryngology of Xiangya Hospital, Central South University, Changsha, 410008 China
- Key Laboratory of Otolaryngology Major Disease Research of Hunan Province, Changsha, 410008 China
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, 410008 China
| | - Ri Su
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
| | - Xuewen Wu
- Department of Otorhinolaryngology of Xiangya Hospital, Central South University, Changsha, 410008 China
- Key Laboratory of Otolaryngology Major Disease Research of Hunan Province, Changsha, 410008 China
- National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, 410008 China
| | - Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205 China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha, 410083 China
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12
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Wen R, Zhang M, Xu R, Gao Y, Liu L, Chen H, Wang X, Zhu W, Lin H, Liu C, Zeng X. COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19. Eur Radiol 2023; 33:3133-3143. [PMID: 36892649 PMCID: PMC9996554 DOI: 10.1007/s00330-023-09498-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/08/2022] [Accepted: 01/29/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.
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Affiliation(s)
- Ru Wen
- Medical College, Guizhou University, Guizhou, 550000, People's Republic of China.,Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.,Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Mudan Zhang
- Guizhou Medical University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Rui Xu
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China
| | - Yingming Gao
- College of Life Science, Guizhou University, Guiyang, Guizhou Province, 550000, People's Republic of China
| | - Lin Liu
- Department of Respiratory Medicine, Guizhou Provincial People Hospital, Guiyang City, Guizhou Province, 550000, People's Republic of China
| | - Hui Chen
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Xingang Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China
| | - Wenyan Zhu
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Huafang Lin
- Medical Department, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, 100191, People's Republic of China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), 30 Gao Tan Yan St, 400038, Chongqing, People's Republic of China.
| | - Xianchun Zeng
- Department of Medical Imaging, Guizhou Provincial People Hospital, No.83, East Zhongshan Road, Nanming District, Guizhou Province, 550000, Guiyang City, People's Republic of China.
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13
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Arias-Garzón D, Tabares-Soto R, Bernal-Salcedo J, Ruz GA. Biases associated with database structure for COVID-19 detection in X-ray images. Sci Rep 2023; 13:3477. [PMID: 36859430 PMCID: PMC9975856 DOI: 10.1038/s41598-023-30174-1] [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: 06/24/2022] [Accepted: 02/17/2023] [Indexed: 03/03/2023] Open
Abstract
Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.
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Affiliation(s)
- Daniel Arias-Garzón
- grid.441739.c0000 0004 0486 2919Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001 Colombia
| | - Reinel Tabares-Soto
- grid.441739.c0000 0004 0486 2919Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001 Colombia ,grid.440617.00000 0001 2162 5606Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, 7941169 Santiago, Chile ,grid.7779.e0000 0001 2290 6370Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, 170001 Colombia
| | - Joshua Bernal-Salcedo
- grid.441739.c0000 0004 0486 2919Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001 Colombia
| | - Gonzalo A. Ruz
- grid.440617.00000 0001 2162 5606Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, 7941169 Santiago, Chile ,grid.512276.5Center of Applied Ecology and Sustainability (CAPES), 8331150 Santiago, Chile ,Data Observatory Foundation, 7941169 Santiago, Chile
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14
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Nakashima M, Uchiyama Y, Minami H, Kasai S. Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs. J Med Radiat Sci 2023; 70:13-20. [PMID: 36334033 PMCID: PMC9877603 DOI: 10.1002/jmrs.631] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images. METHODS In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). CONCLUSIONS We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.
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Affiliation(s)
- Maoko Nakashima
- Graduate School of Health SciencesKumamoto UniversityKumamotoJapan
| | - Yoshikazu Uchiyama
- Department of Medical Image Sciences, Faculty of Life SciencesKumamoto UniversityKumamotoJapan
| | | | - Satoshi Kasai
- Department of Radiological TechnologyNiigata University of Health and WelfareNiigataJapan
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15
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Azad AK, Ahmed I, Ahmed MU. In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13030574. [PMID: 36766679 PMCID: PMC9914163 DOI: 10.3390/diagnostics13030574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/08/2022] [Accepted: 01/17/2023] [Indexed: 02/08/2023] Open
Abstract
The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.
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16
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Suba S, Muthulakshmi M. A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques. NETWORK (BRISTOL, ENGLAND) 2023; 34:26-64. [PMID: 36420865 DOI: 10.1080/0954898x.2022.2147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/27/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 pandemic created a turmoil across nations due to Severe Acute Respiratory Syndrome Corona virus-1(SARS - Co-V-2). The severity of COVID-19 symptoms is starting from cold, breathing problems, issues in respiratory system which may also lead to life threatening situations. This disease is widely contaminating and transmitted from man-to-man. The contamination is spreading when the human organs like eyes, nose, and mouth get in contact with contaminated fluids. This virus can be screened through performing a nasopharyngeal swab test which is time consuming. So the physicians are preferring the fast detection methods like chest radiography images and CT scans. At times some confusion in finding out the accurate disorder from chest radiography images can happen. To overcome this issue this study reviews several deep learning and machine learning procedures to be implemented in X-ray images of chest. This also helps the professionals to find out the other types of malfunctions happening in the chest other than COVID-19 also. This review can act as a guidance to the doctors and radiologists in identifying the COVID-19 and other types of viruses causing illness in the human anatomy and can provide aid soon.
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Affiliation(s)
- Saravanan Suba
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
| | - M Muthulakshmi
- Department of Computer Science, Kamarajar Government Arts College, Tirunelveli, Surandai 627859, India
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17
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Meng Y, Bridge J, Addison C, Wang M, Merritt C, Franks S, Mackey M, Messenger S, Sun R, Fitzmaurice T, McCann C, Li Q, Zhao Y, Zheng Y. Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning. Med Image Anal 2023; 84:102722. [PMID: 36574737 PMCID: PMC9753459 DOI: 10.1016/j.media.2022.102722] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/17/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people's health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network's superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963.
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Affiliation(s)
- Yanda Meng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Joshua Bridge
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Cliff Addison
- Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom
| | - Manhui Wang
- Advanced Research Computing, University of Liverpool, Liverpool, United Kingdom
| | | | - Stu Franks
- Alces Flight Limited, Bicester, United Kingdom
| | - Maria Mackey
- Amazon Web Services, 60 Holborn Viaduct, London, United Kingdom
| | - Steve Messenger
- Amazon Web Services, 60 Holborn Viaduct, London, United Kingdom
| | - Renrong Sun
- Department of Radiology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Hubei University of Chinese Medicine, Wuhan, China
| | - Thomas Fitzmaurice
- Adult Cystic Fibrosis Unit, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, United Kingdom
| | - Caroline McCann
- Radiology, Liverpool Heart and Chest Hospital NHS Foundation Trust, United Kingdom
| | - Qiang Li
- The Affiliated People’s Hospital of Ningbo University, Ningbo, China
| | - Yitian Zhao
- The Affiliated People's Hospital of Ningbo University, Ningbo, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science, Ningbo, China.
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
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18
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Ullah Z, Usman M, Latif S, Gwak J. Densely attention mechanism based network for COVID-19 detection in chest X-rays. Sci Rep 2023; 13:261. [PMID: 36609667 PMCID: PMC9816547 DOI: 10.1038/s41598-022-27266-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 12/29/2022] [Indexed: 01/09/2023] Open
Abstract
Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
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Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Siddique Latif
- Faculty of Health and Computing, University of Southern Queensland, Toowoomba, QLD, 4300, Australia
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju, 27469, South Korea.
- Department of IT. Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju, 27469, South Korea.
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19
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Chaddad A, Peng J, Xu J, Bouridane A. Survey of Explainable AI Techniques in Healthcare. SENSORS (BASEL, SWITZERLAND) 2023; 23:634. [PMID: 36679430 PMCID: PMC9862413 DOI: 10.3390/s23020634] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/29/2022] [Indexed: 05/27/2023]
Abstract
Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whether a patient is sick before forming a reasonable explanation based on the patient's symptoms and/or an examination. Therefore, to be a viable and accepted tool, AI needs to mimic human judgment and interpretation skills. Specifically, explainable AI (XAI) aims to explain the information behind the black-box model of deep learning that reveals how the decisions are made. This paper provides a survey of the most recent XAI techniques used in healthcare and related medical imaging applications. We summarize and categorize the XAI types, and highlight the algorithms used to increase interpretability in medical imaging topics. In addition, we focus on the challenging XAI problems in medical applications and provide guidelines to develop better interpretations of deep learning models using XAI concepts in medical image and text analysis. Furthermore, this survey provides future directions to guide developers and researchers for future prospective investigations on clinical topics, particularly on applications with medical imaging.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
- The Laboratory for Imagery Vision and Artificial Intelligence, Ecole de Technologie Superieure, 1100 Rue Notre Dame O, Montreal, QC H3C 1K3, Canada
| | - Jihao Peng
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
| | - Jian Xu
- School of Artificial Intelligence, Guilin University of Electronic Technology, Jinji Road, Guilin 541004, China
| | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates
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20
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Aslan MF. A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 231:104695. [PMID: 36311473 PMCID: PMC9595502 DOI: 10.1016/j.chemolab.2022.104695] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/16/2022] [Accepted: 10/17/2022] [Indexed: 05/06/2023]
Abstract
This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.
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Affiliation(s)
- Muhammet Fatih Aslan
- Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey
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21
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Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images. Comput Biol Med 2022; 151:106324. [PMID: 36423531 PMCID: PMC9671873 DOI: 10.1016/j.compbiomed.2022.106324] [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: 09/07/2022] [Revised: 10/27/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.
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22
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Wang C, Wang X, Wang Z, Zhu W, Hu R. COVID-19 contact tracking by group activity trajectory recovery over camera networks. PATTERN RECOGNITION 2022; 132:108908. [PMID: 35873066 PMCID: PMC9290376 DOI: 10.1016/j.patcog.2022.108908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 07/14/2022] [Accepted: 07/16/2022] [Indexed: 05/03/2023]
Abstract
Contact tracking plays an important role in the epidemiological investigation of COVID-19, which can effectively reduce the spread of the epidemic. As an excellent alternative method for contact tracking, mobile phone location-based methods are widely used for locating and tracking contacts. However, current inaccurate positioning algorithms that are widely used in contact tracking lead to the inaccurate follow-up of contacts. Aiming to achieve accurate contact tracking for the COVID-19 contact group, we extend the analysis of the GPS data to combine GPS data with video surveillance data and address a novel task named group activity trajectory recovery. Meanwhile, a new dataset called GATR-GPS is constructed to simulate a realistic scenario of COVID-19 contact tracking, and a coordinated optimization algorithm with a spatio-temporal constraint table is further proposed to realize efficient trajectory recovery of pedestrian trajectories. Extensive experiments on the novel collected dataset and commonly used two existing person re-identification datasets are performed, and the results evidently demonstrate that our method achieves competitive results compared to the state-of-the-art methods.
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Affiliation(s)
- Chao Wang
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China
| | - XiaoChen Wang
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Zhongyuan Wang
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China
| | - WenQian Zhu
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China
| | - Ruimin Hu
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China
- Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan 430072, China
- Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
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23
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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Fan L, Yang W, Tu W, Zhou X, Zou Q, Zhang H, Feng Y, Liu S. Thoracic Imaging in China: Yesterday, Today, and Tomorrow. J Thorac Imaging 2022; 37:366-373. [PMID: 35980382 PMCID: PMC9592175 DOI: 10.1097/rti.0000000000000670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Thoracic imaging has been revolutionized through advances in technology and research around the world, and so has China. Thoracic imaging in China has progressed from anatomic observation to quantitative and functional evaluation, from using traditional approaches to using artificial intelligence. This article will review the past, present, and future of thoracic imaging in China, in an attempt to establish new accepted strategies moving forward.
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Affiliation(s)
- Li Fan
- Second Affiliated Hospital, Naval Medical University
| | - Wenjie Yang
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenting Tu
- Second Affiliated Hospital, Naval Medical University
| | - Xiuxiu Zhou
- Second Affiliated Hospital, Naval Medical University
| | - Qin Zou
- Second Affiliated Hospital, Naval Medical University
| | - Hanxiao Zhang
- Second Affiliated Hospital, Naval Medical University
| | - Yan Feng
- Second Affiliated Hospital, Naval Medical University
| | - Shiyuan Liu
- Second Affiliated Hospital, Naval Medical University
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25
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Sharma A, Mishra PK. Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. PATTERN RECOGNITION 2022; 131:108826. [PMID: 35698723 PMCID: PMC9170279 DOI: 10.1016/j.patcog.2022.108826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 04/24/2022] [Accepted: 06/02/2022] [Indexed: 05/17/2023]
Abstract
The devastating outbreak of Coronavirus Disease (COVID-19) cases in early 2020 led the world to face health crises. Subsequently, the exponential reproduction rate of COVID-19 disease can only be reduced by early diagnosis of COVID-19 infection cases correctly. The initial research findings reported that radiological examinations using CT and CXR modality have successfully reduced false negatives by RT-PCR test. This research study aims to develop an explainable diagnosis system for the detection and infection region quantification of COVID-19 disease. The existing research studies successfully explored deep learning approaches with higher performance measures but lacked generalization and interpretability for COVID-19 diagnosis. In this study, we address these issues by the Covid-MANet network, an automated end-to-end multi-task attention network that works for 5 classes in three stages for COVID-19 infection screening. The first stage of the Covid-MANet network localizes attention of the model to the relevant lungs region for disease recognition. The second stage of the Covid-MANet network differentiates COVID-19 cases from bacterial pneumonia, viral pneumonia, normal and tuberculosis cases, respectively. To improve the interpretation and explainability, three experiments have been conducted in exploration of the most coherent and appropriate classification approach. Moreover, the multi-scale attention model MA-DenseNet201 proposed for the classification of COVID-19 cases. The final stage of the Covid-MANet network quantifies the proportion of infection and severity of COVID-19 in the lungs. The COVID-19 cases are graded into more specific severity levels such as mild, moderate, severe, and critical as per the score assigned by the RALE scoring system. The MA-DenseNet201 classification model outperforms eight state-of-the-art CNN models, in terms of sensitivity and interpretation with lung localization network. The COVID-19 infection segmentation by UNet with DenseNet121 encoder achieves dice score of 86.15% outperforming UNet, UNet++, AttentionUNet, R2UNet, with VGG16, ResNet50 and DenseNet201 encoder. The proposed network not only classifies images based on the predicted label but also highlights the infection by segmentation/localization of model-focused regions to support explainable decisions. MA-DenseNet201 model with a segmentation-based cropping approach achieves maximum interpretation of 96% with COVID-19 sensitivity of 97.75%. Finally, based on class-varied sensitivity analysis Covid-MANet ensemble network of MA-DenseNet201, ResNet50 and MobileNet achieve 95.05% accuracy and 98.75% COVID-19 sensitivity. The proposed model is externally validated on an unseen dataset, yields 98.17% COVID-19 sensitivity.
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Affiliation(s)
- Ajay Sharma
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
| | - Pramod Kumar Mishra
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
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26
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Novakovic A, Marshall AH. The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions. PATTERN RECOGNITION 2022; 130:108790. [PMID: 35601479 PMCID: PMC9107333 DOI: 10.1016/j.patcog.2022.108790] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 05/16/2023]
Abstract
The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.
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Affiliation(s)
- Aleksandar Novakovic
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
| | - Adele H Marshall
- School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, BT7 1NN, Northern Ireland, United Kingdom
- Joint Research Centre in AI for Health and Wellness, Faculty of Business and IT, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario L1G 0C5, Canada
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27
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Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2022; 43:486-510. [PMID: 34336141 PMCID: PMC8312058 DOI: 10.1016/j.irbm.2021.07.002] [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: 02/15/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 12/24/2022]
Abstract
Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - M Hariharan
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India
| | - G S Kathiresan
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India
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Koh SJT, Nafea M, Nugroho H. Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray. ADVANCES IN COMPUTATIONAL INTELLIGENCE 2022; 2:33. [PMID: 36187081 PMCID: PMC9516511 DOI: 10.1007/s43674-022-00044-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 05/17/2022] [Accepted: 09/01/2022] [Indexed: 10/29/2022]
Abstract
Due to the outbreak of COVID-19 disease globally, countries around the world are facing shortages of resources (i.e. testing kits, medicine). A quick diagnosis of COVID-19 and isolating patients are crucial in curbing the pandemic, especially in rural areas. This is because the disease is highly contagious and can spread easily. To assist doctors, several studies have proposed an initial detection of COVID-19 cases using radiological images. In this paper, we propose an alternative method for analyzing chest X-ray images to provide an efficient and accurate diagnosis of COVID-19 which can run on edge devices. The approach acts as an enabler for the deep learning model to be deployed in practical application. Here, the convolutional neural network models which are fine-tuned to predict COVID-19 and pneumonia infection from chest X-ray images are developed by adopting transfer learning techniques. The developed model yielded an accuracy of 98.13%, sensitivity of 97.7%, and specificity of 99.1%. To highlight the important regions in the X-ray images which directs the model to its decision/prediction, we adopted the Gradient Class Activation Map (Grad-CAM). The generated heat maps from the Grad-CAM were then compared with the annotated X-ray images by board-certified radiologists. Results showed that the findings strongly correlate with clinical evidence. For practical deployment, we implemented the trained model in edge devices (NCS2) and this has achieved an improvement of 90% in inference speed compared to CPU. This shows that the developed model has the potential to be implemented on the edge, for example in primary care clinics and rural areas which are not well-equipped or do not have access to stable internet connections.
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Affiliation(s)
- Shaline Jia Thean Koh
- Present Address: Department of Electrical and Electronic Engineering, University of Nottingham Malaysia, Semenyih, 43500 Malaysia
| | - Marwan Nafea
- Present Address: Department of Electrical and Electronic Engineering, University of Nottingham Malaysia, Semenyih, 43500 Malaysia
| | - Hermawan Nugroho
- Present Address: Department of Electrical and Electronic Engineering, University of Nottingham Malaysia, Semenyih, 43500 Malaysia
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29
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Shah A, Shah M. Advancement of deep learning in pneumonia/Covid-19 classification and localization: A systematic review with qualitative and quantitative analysis. Chronic Dis Transl Med 2022; 8:154-171. [PMID: 35572951 PMCID: PMC9086991 DOI: 10.1002/cdt3.17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 01/20/2022] [Indexed: 12/15/2022] Open
Abstract
Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid-19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X-ray) and Covid-19 (real-time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid-19 can be detected instantly from chest X-rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid-19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community-acquired pneumonia, viral pneumonia, and Covid-19 from images of chest X-rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one-stop solution to beginners and current researchers interested in this field.
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Affiliation(s)
- Aakash Shah
- Department of Computer Science & Engineering, Institute of TechnologyNirma UniversityAhmedabadIndia
| | - Manan Shah
- Department of Chemical Engineering, School of TechnologyPandit Deendayal Energy UniversityGandhinagarIndia
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30
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Rabie AH, Mansour NA, Saleh AI, Takieldeen AE. Expecting individuals' body reaction to Covid-19 based on statistical Naïve Bayes technique. PATTERN RECOGNITION 2022; 128:108693. [PMID: 35400761 PMCID: PMC8983097 DOI: 10.1016/j.patcog.2022.108693] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 02/01/2022] [Accepted: 04/03/2022] [Indexed: 06/14/2023]
Abstract
Covid-19, what a strange, unpredictable mutated virus. It has baffled many scientists, as no firm rule has yet been reached to predict the effect that the virus can inflict on people if they are infected with it. Recently, many researches have been introduced for diagnosing Covid-19; however, none of them pay attention to predict the effect of the virus on the person's body if the infection occurs but before the infection really takes place. Predicting the extent to which people will be affected if they are infected with the virus allows for some drastic precautions to be taken for those who will suffer from serious complications, while allowing some freedom for those who expect not to be affected badly. This paper introduces Covid-19 Prudential Expectation Strategy (CPES) as a new strategy for predicting the behavior of the person's body if he has been infected with Covid-19. The CPES composes of three phases called Outlier Rejection Phase (ORP), Feature Selection Phase (FSP), and Classification Phase (CP). For enhancing the classification accuracy in CP, CPES employs two proposed techniques for outlier rejection in ORP and feature selection in FSP, which are called Hybrid Outlier Rejection (HOR) method and Improved Binary Genetic Algorithm (IBGA) method respectively. In ORP, HOR rejects outliers in the training data using a hybrid method that combines standard division and Binary Gray Wolf Optimization (BGWO) method. On the other hand, in FSP, IBGA as a hybrid method selects the most useful features for the prediction process. IBGA includes Fisher Score (FScore) as a filter method to quickly select the features and BGA as a wrapper method to accurately select the features based on the average accuracy value from several classification models as a fitness function to guarantee the efficiency of the selected subset of features with any classifier. In CP, CPES has the ability to classify people based on their bodies' reaction to Covid-19 infection, which is built upon a proposed Statistical Naïve Bayes (SNB) classifier after performing the previous two phases. CPES has been compared against recent related strategies in terms of accuracy, error, recall, precision, and run-time using Covid-19 dataset [1]. This dataset contains routine blood tests collected from people before and after their infection with covid-19 through a Web-based form created by us. CPES outperforms the competing methods in experimental results because it provides the best results with values of 0.87, 0.13, 0.84, and 0.79 for accuracy, error, precision, and recall.
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Affiliation(s)
- Asmaa H Rabie
- Computers and Control Dept. faculty of engineering Mansoura University, Mansoura, Egypt
| | - Nehal A Mansour
- Nile Higher Institute for Engineering and Technology, Artificial intelligence Lab., Mansoura, Egypt
| | - Ahmed I Saleh
- Computers and Control Dept. faculty of engineering Mansoura University, Mansoura, Egypt
| | - Ali E Takieldeen
- IEEE Senior Member, Faculty of Artificial Intelligence, Delta University For Science and Technology, Egypt
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31
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Abir FF, Alyafei K, Chowdhury MEH, Khandakar A, Ahmed R, Hossain MM, Mahmud S, Rahman A, Abbas TO, Zughaier SM, Naji KK. PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data. Comput Biol Med 2022; 147:105682. [PMID: 35714504 PMCID: PMC9170596 DOI: 10.1016/j.compbiomed.2022.105682] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/19/2022] [Accepted: 05/30/2022] [Indexed: 11/16/2022]
Abstract
While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.
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Affiliation(s)
- Farhan Fuad Abir
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Khalid Alyafei
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Biomedical Research Centre, Qatar University, Doha, 2713, Qatar
| | | | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Ashiqur Rahman
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Japan
| | - Tareq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar, 26999
| | - Susu M Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, 2713, Qatar
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Mehrabi S, Rahmanian J, Jalli R. The Accuracy of Lung Ultrasonography Diagnosis of Community-Acquired Pneumonia, in an Adult Cohort. JOURNAL OF DIAGNOSTIC MEDICAL SONOGRAPHY 2022. [DOI: 10.1177/87564793221115197] [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
Objective: Community-acquired pneumonia (CAP) is a common respiratory infection, and diagnosis is frequently performed using a chest radiography (CXR). Sonography is an available method with less radiation exposure, but has not been confirmed for diagnosis of CAP. The objective was to compare the diagnostic accuracy of sonography. Materials and Methods: In this cross-sectional study, 90 adult patients (aged >18 years) were admitted to the emergency department of two university-affiliated hospitals in Southwest Iran, from July to December 2019, with a confirmed diagnosis of CAP. The patient symptoms and CXR results were included as part of this study. Within 24 hours after obtaining a CXR, a lung ultrasonogram (LUS) was performed. The diagnostic accuracy of semiquantitative LUS (SQLUS) was compared with CXR results using the Pearson chi-square test and Fisher’s exact test. Results: The mean age of participants was 52.98 ± 16.77 years. 51 were men (56.7%). 28 patients (31.1%), who had abnormal SQLUS results, were not associated with CXR findings ( P = .296). SQLUS showed poor diagnostic accuracy for LUS (31.11%). Conclusion: This study results could not confirm LUS as an accurate method for diagnosing CAP in adult patients; although due to the convenient sample of adults and clinical-based diagnosis of CAP, any generalization of the results should be made with caution.
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Affiliation(s)
- Samrad Mehrabi
- Division of Pulmonology, Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Jila Rahmanian
- Division of Pulmonology, Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Reza Jalli
- Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
- Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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Chandra TB, Singh BK, Jain D. Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106947. [PMID: 35749885 PMCID: PMC9403875 DOI: 10.1016/j.cmpb.2022.106947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. METHODS The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. RESULTS The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (JI) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. CONCLUSIONS The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results.
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Affiliation(s)
- Tej Bahadur Chandra
- Department of Computer Applications, National Institute of Technology Raipur, Chhattisgarh, India.
| | - Bikesh Kumar Singh
- Department of Biomedical Engineering, National Institute of Technology Raipur, Chhattisgarh, India
| | - Deepak Jain
- Department of Radiodiagnosis, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, Chhattisgarh, India
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34
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Dentamaro V, Giglio P, Impedovo D, Moretti L, Pirlo G. AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath. PATTERN RECOGNITION 2022; 127:108656. [PMID: 35313619 PMCID: PMC8920577 DOI: 10.1016/j.patcog.2022.108656] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 05/09/2023]
Abstract
This study presents the Auditory Cortex ResNet (AUCO ResNet), it is a biologically inspired deep neural network especially designed for sound classification and more specifically for Covid-19 recognition from audio tracks of coughs and breaths. Differently from other approaches, it can be trained end-to-end thus optimizing (with gradient descent) all the modules of the learning algorithm: mel-like filter design, feature extraction, feature selection, dimensionality reduction and prediction. This neural network includes three attention mechanisms namely the squeeze and excitation mechanism, the convolutional block attention module, and the novel sinusoidal learnable attention. The attention mechanism is able to merge relevant information from activation maps at various levels of the network. The net takes as input raw audio files and it is able to fine tune also the features extraction phase. In fact, a Mel-like filter is designed during the training, thus adapting filter banks on important frequencies. AUCO ResNet has proved to provide state of art results on many datasets. Firstly, it has been tested on many datasets containing Covid-19 cough and breath. This choice is related to the fact that that cough and breath are language independent, allowing for cross dataset tests with generalization aims. These tests demonstrate that the approach can be adopted as a low cost, fast and remote Covid-19 pre-screening tool. The net has also been tested on the famous UrbanSound 8K dataset, achieving state of the art accuracy without any data preprocessing or data augmentation technique.
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Affiliation(s)
- Vincenzo Dentamaro
- Università degli studi di Bari "Aldo Moro", Department of Computer Science, via Orabona 4, Bari, 70125, Italy
| | - Paolo Giglio
- Università degli studi di Bari "Aldo Moro", Department of Computer Science, via Orabona 4, Bari, 70125, Italy
| | - Donato Impedovo
- Università degli studi di Bari "Aldo Moro", Department of Computer Science, via Orabona 4, Bari, 70125, Italy
| | - Luigi Moretti
- Università degli studi di Bari "Aldo Moro", Medical School, Bari, Italy
| | - Giuseppe Pirlo
- Università degli studi di Bari "Aldo Moro", Department of Computer Science, via Orabona 4, Bari, 70125, Italy
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Szepesi P, Szilágyi L. Detection of pneumonia using convolutional neural networks and deep learning. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Mondal AK. COVID-19 prognosis using limited chest X-ray images. Appl Soft Comput 2022; 122:108867. [PMID: 35494338 PMCID: PMC9035620 DOI: 10.1016/j.asoc.2022.108867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 01/18/2022] [Accepted: 04/09/2022] [Indexed: 01/31/2023]
Abstract
The COrona VIrus Disease 2019 (COVID-19) pandemic is an ongoing global pandemic that has claimed millions of lives till date. Detecting COVID-19 and isolating affected patients at an early stage is crucial to contain its rapid spread. Although accurate, the primary viral test 'Reverse Transcription Polymerase Chain Reaction' (RT-PCR) for COVID-19 diagnosis has an elaborate test kit, and the turnaround time is high. This has motivated the research community to develop CXR based automated COVID-19 diagnostic methodologies. However, COVID-19 being a novel disease, there is no annotated large-scale CXR dataset for this particular disease. To address the issue of limited data, we propose to exploit a large-scale CXR dataset collected in the pre-COVID era and train a deep neural network in a self-supervised fashion to extract CXR specific features. Further, we compute attention maps between the global and the local features of the backbone convolutional network while finetuning using a limited COVID-19 CXR dataset. We empirically demonstrate the effectiveness of the proposed method. We provide a thorough ablation study to understand the effect of each proposed component. Finally, we provide visualizations highlighting the critical patches instrumental to the predictive decision made by our model. These saliency maps are not only a stepping stone towards explainable AI but also aids radiologists in localizing the infected area.
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Aggarwal P, Mishra NK, Fatimah B, Singh P, Gupta A, Joshi SD. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Biol Med 2022; 144:105350. [PMID: 35305501 PMCID: PMC8890789 DOI: 10.1016/j.compbiomed.2022.105350] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/10/2022] [Accepted: 02/22/2022] [Indexed: 12/16/2022]
Abstract
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification.
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Affiliation(s)
| | | | - Binish Fatimah
- The Department of ECE, CMR Institute of Technology, Bengaluru, India.
| | - Pushpendra Singh
- The Department of ECE, National Institute of Technology Hamirpur, HP, India.
| | - Anubha Gupta
- The Department of ECE, IIIT-Delhi, Delhi, 110020, India.
| | - Shiv Dutt Joshi
- The Department of EE, Indian Institute of Technology Delhi, Delhi 110016, India.
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Álvarez-Rodríguez L, Moura JD, Novo J, Ortega M. Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening? BMC Med Res Methodol 2022; 22:125. [PMID: 35484483 PMCID: PMC9046709 DOI: 10.1186/s12874-022-01578-w] [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: 10/08/2021] [Accepted: 03/21/2022] [Indexed: 11/10/2022] Open
Abstract
Background The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of the most common resources is the use of different lung imaging modalities (like chest radiography) to explore the possible affected areas. Methods The study of patient characteristics like sex and age in pathologies of this type is crucial for gaining knowledge of the disease and for avoiding biases due to the clear scarcity of data when developing representative systems. In this work, we performed an analysis of these factors in chest X-ray images to identify biases. Specifically, 11 imbalance scenarios were defined with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for training for the age factor. In each study, 3 different approaches for automatic COVID-19 screening were used: Normal vs COVID-19, Pneumonia vs COVID-19 and Non-COVID-19 vs COVID-19. The study was validated using two public chest X-ray datasets, allowing a reliable analysis to support the clinical decision-making process. Results The results for the sex-related analysis indicate this factor slightly affects the system in the Normal VS COVID-19 and Pneumonia VS COVID-19 approaches, although the identified differences are not relevant enough to worsen considerably the system. Regarding the age-related analysis, this factor was observed to be influencing the system in a more consistent way than the sex factor, as it was present in all considered scenarios. However, this worsening does not represent a major factor, as it is not of great magnitude. Conclusions Multiple studies have been conducted in other fields in order to determine if certain patient characteristics such as sex or age influenced these deep learning systems. However, to the best of our knowledge, this study has not been done for COVID-19 despite the urgency and lack of COVID-19 chest x-ray images. The presented results evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01578-w).
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Affiliation(s)
- Lorena Álvarez-Rodríguez
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Joaquim de Moura
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain. .,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, A Coruña, 15071, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, 15006, Spain
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COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6216273. [PMID: 35422979 PMCID: PMC9002900 DOI: 10.1155/2022/6216273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 02/11/2022] [Indexed: 12/12/2022]
Abstract
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed "COV-DLS" consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3. An attempt is made to modify these neoteric architectures by incorporating the average pooling and dense layers. The dropout layer is also added to prevent the overfitting problem. Two dense layers with different activation functions are also added. Thereafter, the output of these modified models is applied during the classification phase, when COV-DLS are applied on a COVID-19 chest X-ray image data set. Classification accuracy of 98.61% is achieved by Modified-VGG16, 97.22% by Modified-VGG19, 95.13% by Modified-ResNet50, and 99.31% by Modified-InceptionV3. COV-DLS outperforms existing deep learning models in terms of accuracy and F1-score.
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Bao G, Chen H, Liu T, Gong G, Yin Y, Wang L, Wang X. COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment. PATTERN RECOGNITION 2022; 124:108499. [PMID: 34924632 PMCID: PMC8666107 DOI: 10.1016/j.patcog.2021.108499] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 11/11/2021] [Accepted: 12/10/2021] [Indexed: 05/07/2023]
Abstract
There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19. COVID-MTL learns different COVID-19 tasks in parallel through our novel random-weighted loss function, which assigns learning weights under Dirichlet distribution to prevent task dominance; our new 3D real-time augmentation algorithm (Shift3D) introduces space variances for 3D CNN components by shifting low-level feature representations of volumetric inputs in three dimensions; thereby, the MTL framework is able to accelerate convergence and improve joint learning performance compared to single-task models. By only using chest CT scans, COVID-MTL was trained on 930 CT scans and tested on separate 399 cases. COVID-MTL achieved AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, which outperformed the state-of-the-art models. Meanwhile, COVID-MTL yielded AUC of 0.800 ± 0.020 and 0.813 ± 0.021 (with transfer learning) for classifying control/suspected, mild/regular, and severe/critically-ill cases. To decipher the recognition mechanism, we also identified high-throughput lung features that were significantly related (P < 0.001) to the positivity and severity of COVID-19.
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Affiliation(s)
- Guoqing Bao
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia
| | - Huai Chen
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tongliang Liu
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia
| | - Guanzhong Gong
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Lisheng Wang
- Department of Automation, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, J12/1 Cleveland St, Darlington, Sydney, NSW 2008, Australia
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Fritz C, Dorigatti E, Rügamer D. Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany. Sci Rep 2022; 12:3930. [PMID: 35273252 PMCID: PMC8913758 DOI: 10.1038/s41598-022-07757-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/23/2022] [Indexed: 12/11/2022] Open
Abstract
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. In this context, several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches often apply standard models of the respective research field, frequently restricting modeling possibilities. For instance, most statistical or epidemiological models cannot directly incorporate unstructured data sources, including relational data that may encode human mobility. In contrast, machine learning approaches may yield better predictions by exploiting these data structures yet lack intuitive interpretability as they are often categorized as black-box models. We propose a combination of both research directions and present a multimodal learning framework that amalgamates statistical regression and machine learning models for predicting local COVID-19 cases in Germany. Results and implications: the novel approach introduced enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest mean squared error scores throughout the observational period in the reported benchmark study. The results corroborate that during most of the observational period more dispersed meeting patterns and a lower percentage of people staying put are associated with higher infection rates. Moreover, the analysis underpins the necessity of including mobility data and showcases the flexibility and interpretability of the proposed approach.
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Affiliation(s)
- Cornelius Fritz
- Department of Statistics, Ludwig Maximilian Universität, München, Germany
| | - Emilio Dorigatti
- Department of Statistics, Ludwig Maximilian Universität, München, Germany
- Institute for Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - David Rügamer
- Department of Statistics, Ludwig Maximilian Universität, München, Germany.
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Aslan MF, Sabanci K, Durdu A, Unlersen MF. COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Comput Biol Med 2022; 142:105244. [PMID: 35077936 PMCID: PMC8770389 DOI: 10.1016/j.compbiomed.2022.105244] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 01/17/2022] [Accepted: 01/17/2022] [Indexed: 12/16/2022]
Abstract
The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died from this infection. To prevent the spread of the disease, which is still in effect, various restriction decisions have been taken all over the world. In addition, the number of COVID-19 tests has been increased to quarantine infected people. However, due to the problems encountered in the supply of RT-PCR tests and the ease of obtaining Computed Tomography and X-ray images, imaging-based methods have become very popular in the diagnosis of COVID-19. Therefore, studies using these images to classify COVID-19 have increased. This paper presents a classification method for computed tomography chest images in the COVID-19 Radiography Database using features extracted by popular Convolutional Neural Networks (CNN) models (AlexNet, ResNet18, ResNet50, Inceptionv3, Densenet201, Inceptionresnetv2, MobileNetv2, GoogleNet). The determination of hyperparameters of Machine Learning (ML) algorithms by Bayesian optimization, and ANN-based image segmentation are the two main contributions in this study. First of all, lung segmentation is performed automatically from the raw image with Artificial Neural Networks (ANNs). To ensure data diversity, data augmentation is applied to the COVID-19 classes, which are fewer than the other two classes. Then these images are applied as input to five different CNN models. The features extracted from each CNN model are given as input to four different ML algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naive Bayes (NB), and Decision Tree (DT) for classification. To achieve the most successful classification accuracy, the hyperparameters of each ML algorithm are determined using Bayesian optimization. With the classification made using these hyperparameters, the highest success is obtained as 96.29% with the DenseNet201 model and SVM algorithm. The Sensitivity, Precision, Specificity, MCC, and F1-Score metric values for this structure are 0.9642, 0.9642, 0.9812, 0.9641 and 0.9453, respectively. These results showed that ML methods with the most optimum hyperparameters can produce successful results.
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Affiliation(s)
- Muhammet Fatih Aslan
- Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey
| | - Kadir Sabanci
- Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.
| | - Akif Durdu
- Electrical and Electronics Engineering, Konya Technical University, Konya, Turkey
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Liu S, Han J, Puyal EL, Kontaxis S, Sun S, Locatelli P, Dineley J, Pokorny FB, Costa GD, Leocani L, Guerrero AI, Nos C, Zabalza A, Sørensen PS, Buron M, Magyari M, Ranjan Y, Rashid Z, Conde P, Stewart C, Folarin AA, Dobson RJ, Bailón R, Vairavan S, Cummins N, Narayan VA, Hotopf M, Comi G, Schuller B, Consortium RC. Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder. PATTERN RECOGNITION 2022; 123:108403. [PMID: 34720200 PMCID: PMC8547790 DOI: 10.1016/j.patcog.2021.108403] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 08/30/2021] [Accepted: 10/24/2021] [Indexed: 05/19/2023]
Abstract
This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3 % , a sensitivity of 100 % and a specificity of 90.6 % , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.
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Affiliation(s)
- Shuo Liu
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Jing Han
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Estela Laporta Puyal
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | - Spyridon Kontaxis
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | - Shaoxiong Sun
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Patrick Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - Judith Dineley
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Gloria Dalla Costa
- Università Vita Salute San Raffaele and Experimental Neurophysiology Unit, Institute of Experimental Neurology, Scientific Institute Hospital San Raffaele, Milan, Italy
| | - Letizia Leocani
- Università Vita Salute San Raffaele and Experimental Neurophysiology Unit, Institute of Experimental Neurology, Scientific Institute Hospital San Raffaele, Milan, Italy
| | - Ana Isabel Guerrero
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Carlos Nos
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Per Soelberg Sørensen
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mathias Buron
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Yatharth Ranjan
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Amos A Folarin
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Richard Jb Dobson
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Raquel Bailón
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | | | - Nicholas Cummins
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Matthew Hotopf
- The Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Giancarlo Comi
- Università Vita Salute San Raffaele, Casa di Cura Privata del Policlinico, Milan, Italy
| | - Björn Schuller
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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Awassa L, Jdey I, Dhahri H, Hcini G, Mahmood A, Othman E, Haneef M. Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights. SENSORS (BASEL, SWITZERLAND) 2022; 22:1890. [PMID: 35271037 PMCID: PMC8915023 DOI: 10.3390/s22051890] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.
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Affiliation(s)
- Lamia Awassa
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Imen Jdey
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Habib Dhahri
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Ghazala Hcini
- Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan 3100, Tunisia; (L.A.); (I.J.); (G.H.)
| | - Awais Mahmood
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Esam Othman
- Department of Information Science, College of Applied Computer Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (A.M.); (E.O.)
| | - Muhammad Haneef
- Department of Electrical Engineering, Foundation University Islamabad, Islamabad 44000, Pakistan;
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Aviles-Rivero AI, Sellars P, Schönlieb CB, Papadakis N. GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays. PATTERN RECOGNITION 2022; 122:108274. [PMID: 34462610 PMCID: PMC8387569 DOI: 10.1016/j.patcog.2021.108274] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/20/2021] [Accepted: 08/21/2021] [Indexed: 05/07/2023]
Abstract
Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist's mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision.
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Affiliation(s)
| | - Philip Sellars
- DAMTP, Faculty of Mathematics, University of Cambridge, UK
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Qi Q, Qi S, Wu Y, Li C, Tian B, Xia S, Ren J, Yang L, Wang H, Yu H. Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images. Comput Biol Med 2022; 141:105182. [PMID: 34979404 PMCID: PMC8715632 DOI: 10.1016/j.compbiomed.2021.105182] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/25/2021] [Accepted: 12/25/2021] [Indexed: 01/08/2023]
Abstract
Background Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological requirements. Methods A fully automatic pipeline of deep learning is proposed for distinguishing COVID-19 from CAP using CT images. Inspired by the diagnostic process of radiologists, the pipeline comprises four connected modules for lung segmentation, selection of slices with lesions, slice-level prediction, and patient-level prediction. The roles of the first and second modules and the effectiveness of the capsule network for slice-level prediction were investigated. A dataset of 326 CT scans was collected to train and test the pipeline. Another public dataset of 110 patients was used to evaluate the generalization capability. Results LinkNet exhibited the largest intersection over union (0.967) and Dice coefficient (0.983) for lung segmentation. For the selection of slices with lesions, the capsule network with the ResNet50 block achieved an accuracy of 92.5% and an area under the curve (AUC) of 0.933. The capsule network using the DenseNet121 block demonstrated better performance for slice-level prediction, with an accuracy of 97.1% and AUC of 0.992. For both datasets, the prediction accuracy of our pipeline was 100% at the patient level. Conclusions The proposed fully automatic deep learning pipeline of deep learning can distinguish COVID-19 from CAP via CT images rapidly and accurately, thereby accelerating diagnosis and augmenting the performance of radiologists. This pipeline is convenient for use by radiologists and provides explainable predictions.
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Affiliation(s)
- Qianqian Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Bin Tian
- Department of Radiology, The Second People's Hospital of Guiyang, Guiyang, China.
| | - Shuyue Xia
- Department of Respiratory Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China.
| | - Jigang Ren
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
| | - Liming Yang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
| | - Hanlin Wang
- Department of Radiology, General Hospital of the Yangtze River Shipping, Wuhan, China.
| | - Hui Yu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China; Department of Radiology, The Seventh Affiliated Hospital, Southern Medical University, Guangzhou, China.
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Deshpande G, Batliner A, Schuller BW. AI-Based human audio processing for COVID-19: A comprehensive overview. PATTERN RECOGNITION 2022; 122:108289. [PMID: 34483372 PMCID: PMC8404390 DOI: 10.1016/j.patcog.2021.108289] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 06/02/2023]
Abstract
The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using 'Artificial Intelligence' techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.
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Affiliation(s)
- Gauri Deshpande
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany
- TCS Research Pune, India
| | - Anton Batliner
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany
| | - Björn W Schuller
- Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany
- GLAM - Group on Language, Audio, & Music, Imperial College London, UK
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Shah PM, Ullah F, Shah D, Gani A, Maple C, Wang Y, Abrar M, Islam SU. Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:35094-35105. [PMID: 35582498 PMCID: PMC9088790 DOI: 10.1109/access.2021.3077592] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 04/20/2021] [Indexed: 05/03/2023]
Abstract
In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.
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Affiliation(s)
- Pir Masoom Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Faizan Ullah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Dilawar Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Abdullah Gani
- Faculty of Computer Science and Information TechnologyUniversity of Malaya Kuala Lumpur 50603 Malaysia
- Faculty of Computing and InformaticsUniversity Malaysia Sabah Labuan 88400 Malaysia
| | - Carsten Maple
- Secure Cyber Systems Research Group, WMGUniversity of Warwick Coventry CV4 7AL U.K
- Alan Turing Institute London NW1 2DB U.K
| | - Yulin Wang
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Mohammad Abrar
- Department of Computer ScienceMohi-ud-Din Islamic University Nerian Sharif 12080 Pakistan
| | - Saif Ul Islam
- Department of Computer ScienceInstitute of Space Technology Islamabad 44000 Pakistan
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Shah PM, Ullah F, Shah D, Gani A, Maple C, Wang Y, Abrar M, Islam SU. Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2022; 10:35094-35105. [PMID: 35582498 DOI: 10.1109/access.2021.3089454] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 04/20/2021] [Indexed: 05/20/2023]
Abstract
In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.
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Affiliation(s)
- Pir Masoom Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Faizan Ullah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Dilawar Shah
- Department of Computer ScienceBacha Khan University Charsadda 24000 Pakistan
| | - Abdullah Gani
- Faculty of Computer Science and Information TechnologyUniversity of Malaya Kuala Lumpur 50603 Malaysia
- Faculty of Computing and InformaticsUniversity Malaysia Sabah Labuan 88400 Malaysia
| | - Carsten Maple
- Secure Cyber Systems Research Group, WMGUniversity of Warwick Coventry CV4 7AL U.K
- Alan Turing Institute London NW1 2DB U.K
| | - Yulin Wang
- School of Computer ScienceWuhan University Wuhan 430072 China
| | - Mohammad Abrar
- Department of Computer ScienceMohi-ud-Din Islamic University Nerian Sharif 12080 Pakistan
| | - Saif Ul Islam
- Department of Computer ScienceInstitute of Space Technology Islamabad 44000 Pakistan
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Guarrasi V, D'Amico NC, Sicilia R, Cordelli E, Soda P. Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays. PATTERN RECOGNITION 2022; 121:108242. [PMID: 34393277 PMCID: PMC8351284 DOI: 10.1016/j.patcog.2021.108242] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/26/2021] [Accepted: 08/08/2021] [Indexed: 05/05/2023]
Abstract
The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.
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Affiliation(s)
- Valerio Guarrasi
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy
| | - Natascha Claudia D'Amico
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., Milan, Italy
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy
| | - Rosa Sicilia
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy
| | - Ermanno Cordelli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy
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