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Slika B, Dornaika F, Merdji H, Hammoudi K. Lung pneumonia severity scoring in chest X-ray images using transformers. Med Biol Eng Comput 2024:10.1007/s11517-024-03066-3. [PMID: 38589723 DOI: 10.1007/s11517-024-03066-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/24/2024] [Indexed: 04/10/2024]
Abstract
To create robust and adaptable methods for lung pneumonia diagnosis and the assessment of its severity using chest X-rays (CXR), access to well-curated, extensive datasets is crucial. Many current severity quantification approaches require resource-intensive training for optimal results. Healthcare practitioners require efficient computational tools to swiftly identify COVID-19 cases and predict the severity of the condition. In this research, we introduce a novel image augmentation scheme as well as a neural network model founded on Vision Transformers (ViT) with a small number of trainable parameters for quantifying COVID-19 severity and other lung diseases. Our method, named Vision Transformer Regressor Infection Prediction (ViTReg-IP), leverages a ViT architecture and a regression head. To assess the model's adaptability, we evaluate its performance on diverse chest radiograph datasets from various open sources. We conduct a comparative analysis against several competing deep learning methods. Our results achieved a minimum Mean Absolute Error (MAE) of 0.569 and 0.512 and a maximum Pearson Correlation Coefficient (PC) of 0.923 and 0.855 for the geographic extent score and the lung opacity score, respectively, when the CXRs from the RALO dataset were used in training. The experimental results reveal that our model delivers exceptional performance in severity quantification while maintaining robust generalizability, all with relatively modest computational requirements. The source codes used in our work are publicly available at https://github.com/bouthainas/ViTReg-IP .
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Affiliation(s)
- Bouthaina Slika
- University of the Basque Country UPV/EHU, San Sebastian, Spain
- Lebanese International University, Beirut, Lebanon
- Beirut International University, Beirut, Lebanon
| | - Fadi Dornaika
- University of the Basque Country UPV/EHU, San Sebastian, Spain.
- IKERBASQUE, Basque Foundation for Science, Bilbao, Spain.
| | - Hamid Merdji
- INSERM, UMR 1260, Regenerative Nanomedicine (RNM), CRBS, University of Strasbourg, Strasbourg, France
- Hôpital Universitaire de Strasbourg, Strasbourg, France
| | - Karim Hammoudi
- Université de Haute-Alsace IRIMAS, Mulhouse, France
- University of Strasbourg, Strasbourg, France
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2
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Wu L, Zhang J, Wang Y, Ding R, Cao Y, Liu G, Liufu C, Xie B, Kang S, Liu R, Li W, Guan F. Pneumonia detection based on RSNA dataset and anchor-free deep learning detector. Sci Rep 2024; 14:1929. [PMID: 38253758 PMCID: PMC10803753 DOI: 10.1038/s41598-024-52156-7] [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: 07/11/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.
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Affiliation(s)
- Linghua Wu
- Internal Medicine Department, Taizhou Fifth People's Hospital, Taizhou, China
| | - Jing Zhang
- Respiratory and Critical Care Medicine, Taizhou Fourth People's Hospital, Taizhou, China
| | - Yilin Wang
- Internal Medicine Department, Taizhou Fifth People's Hospital, Taizhou, China
| | - Rong Ding
- Respiratory and Critical Care Medicine, Taizhou Fourth People's Hospital, Taizhou, China
| | - Yueqin Cao
- Respiratory and Critical Care Medicine, Taizhou Fourth People's Hospital, Taizhou, China
| | - Guiqin Liu
- Respiratory and Critical Care Medicine, Taizhou Fourth People's Hospital, Taizhou, China
| | - Changsheng Liufu
- Department of Gerontology, Dongguan First Hospital Affiliated to Guangdong Medical University, Dongguan, China
| | - Baowei Xie
- Respiratory and Critical Care Medicine, Taizhou Fourth People's Hospital, Taizhou, China
| | - Shanping Kang
- Department of Gerontolog, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China
| | - Rui Liu
- Respiratory and Critical Care Medicine, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China
| | - Wenle Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
| | - Furen Guan
- Emergency Department, Zhuhai Hospital of Integrated Chinese and Western Medicine, Zhuhai, China.
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Nahar Shaima S, Haque MA, Sarmin M, Nuzhat S, Jahan Y, Bushra Matin F, Shahrin L, Afroze F, Saha H, Timu RT, Kamal M, Shahid ASMSB, Sultana N, Mamun GMS, Chisti MJ, Ahmed T. Performance of chest X-ray scoring in predicting disease severity and outcomes of patients hospitalised with COVID-19 in Bangladesh. SAGE Open Med 2024; 12:20503121231222325. [PMID: 38264406 PMCID: PMC10804927 DOI: 10.1177/20503121231222325] [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: 07/17/2023] [Accepted: 12/06/2023] [Indexed: 01/25/2024] Open
Abstract
Introduction Evaluation of potential outcomes of COVID-19-affected pneumonia patients using computed tomography scans may not be conceivable in low-resource settings. Thus, we aimed to evaluate the performance of chest X-ray scoring in predicting the disease severity and outcomes of adults hospitalised with COVID-19. Methods This was a retrospective chart analysis consuming data from COVID-19-positive adults who had chest X-ray availability and were admitted to a temporary COVID unit, in Bangladesh from 23rd April 2020 to 15th November 2021. At least one clinical intensivist and one radiologist combinedly reviewed each admission chest X-ray for the different lung findings. Chest X-ray scoring varied from 0 to 8, depending on the area of lung involvement with 0 indicating no involvement and 8 indicating ⩾75% involvement of both lungs. The receiver operating characteristic curve was used to determine the optimum chest X-ray cut-off score for predicting the fatal outcomes. Result A total of 218 (82.9%) out of 263 COVID-19-affected adults were included in the study. The receiver operating characteristic curve demonstrated the optimum cut-off as ⩾3 and ⩾5 for disease severity and death, respectively. In multivariate logistic regression analysis, a chest X-ray score of ⩾3 was found to be independently associated with disease severity (aOR: 8.70; 95% CI: 3.82, 19.58, p < 0.001) and a score of ⩾5 with death (aOR: 16.53; 95% CI: 4.74, 57.60, p < 0.001) after adjusting age, sex, antibiotic usage before admission, history of fever, cough, diabetes mellitus, hypertension, total leukocytes count and C-reactive protein. Conclusion Using chest X-ray scoring derived cut-off at admission might help to identify the COVID-19-affected adults who are at risk of severe disease and mortality. This may help to initiate early and aggressive management of such patients, thereby reducing their fatal outcomes.
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Affiliation(s)
- Shamsun Nahar Shaima
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Md Ahshanul Haque
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Monira Sarmin
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Clinical and Diagnostic Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Sharika Nuzhat
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Clinical and Diagnostic Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Yasmin Jahan
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Fariha Bushra Matin
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Lubaba Shahrin
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Farzana Afroze
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Haimanti Saha
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Rehnuma Tabassum Timu
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Mehnaz Kamal
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | | | - Nadia Sultana
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Gazi Md. Salahuddin Mamun
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Mohammod Jobayer Chisti
- Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Clinical and Diagnostic Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Tahmeed Ahmed
- Clinical and Diagnostic Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
- Office of Executive the Director, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
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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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5
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Reshan MSA, Gill KS, Anand V, Gupta S, Alshahrani H, Sulaiman A, Shaikh A. Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model. Healthcare (Basel) 2023; 11:healthcare11111561. [PMID: 37297701 DOI: 10.3390/healthcare11111561] [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: 04/08/2023] [Revised: 05/24/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Pneumonia has been directly responsible for a huge number of deaths all across the globe. Pneumonia shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in the way chest X-ray images are acquired and processed, which can impact the quality and consistency of the images. This can make it challenging to develop robust algorithms that can accurately identify pneumonia in all types of images. Hence, there is a need to develop robust, data-driven algorithms that are trained on large, high-quality datasets and validated using a range of imaging techniques and expert radiological analysis. In this research, a deep-learning-based model is demonstrated for differentiating between normal and severe cases of pneumonia. This complete proposed system has a total of eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These eight pre-trained models were simulated on two datasets having 5856 images and 112,120 images of chest X-rays. The best accuracy is obtained on the MobileNet model with values of 94.23% and 93.75% on two different datasets. Key hyperparameters including batch sizes, number of epochs, and different optimizers have all been considered during comparative interpretation of these models to determine the most appropriate model.
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Affiliation(s)
- Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Kanwarpartap Singh Gill
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
| | - Vatsala Anand
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
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6
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Challenges, opportunities, and advances related to COVID-19 classification based on deep learning. DATA SCIENCE AND MANAGEMENT 2023. [PMCID: PMC10063459 DOI: 10.1016/j.dsm.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.
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7
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Taribagil P, Hogg HDJ, Balaskas K, Keane PA. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. EXPERT REVIEW OF OPHTHALMOLOGY 2023. [DOI: 10.1080/17469899.2023.2175672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Priyal Taribagil
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - HD Jeffry Hogg
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Population Health Science, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Ophthalmology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Road, Newcastle upon Tyne, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
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8
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SuperpixelGridMasks Data Augmentation: Application to Precision Health and Other Real-world Data. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 6:442-460. [PMID: 36688121 PMCID: PMC9838402 DOI: 10.1007/s41666-022-00122-1] [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: 04/29/2022] [Revised: 10/22/2022] [Accepted: 12/01/2022] [Indexed: 01/15/2023]
Abstract
A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models as well as precision health and surrounding real-world datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks.
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VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images. PROCEDIA COMPUTER SCIENCE 2023; 218:1926-1935. [PMID: 36743790 PMCID: PMC9886333 DOI: 10.1016/j.procs.2023.01.169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.
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10
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Mahesh Kumar N, Premalatha K, Suvitha S. Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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11
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Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Khan S, Khan MK, Khan R. Harnessing intelligent technologies to curb COVID-19 pandemic: taxonomy and open challenges. COMPUTING 2023; 105:811-830. [PMCID: PMC8324437 DOI: 10.1007/s00607-021-00983-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/06/2021] [Indexed: 05/22/2023]
Abstract
The world has changed dramatically since the outbreak of COVID-19 pandemic. This has not only affected the humanity, but has also badly damaged the world’s socio-economic system. Currently, people are looking for a magical solution to overcome this pandemic. Similarly, scientists across the globe are working to find remedies to overcome this challenge. The role of technologies is not far behind in this situation, which attracts many sectors from government agencies to medical practitioners, and market analysts. This is quite true that in a few months of time, scientists, researchers, and industrialists have come up with some acceptable innovative solutions and harnessing existing technologies to stop the spread of COVID-19. Therefore, it is pertinent to highlight the role of intelligent technologies, which play a pivotal role in curbing this pandemic. In this paper, we devise a taxonomy related to the technologies being used in the current pandemic. We show that the most prominent technologies are artificial intelligence, machine learning, cloud computing, big data analytics, and blockchain. Moreover, we highlight some key open challenges, which technologists might face to control this outbreak. Finally, we conclude that to impede this pandemic, a collective effort is required from different professionals in support of using existing and new technologies. Finally, we conclude that to stop this pandemic, machine learning approaches with integration of cloud computing using high performance computing could provision the pandemic with minimum cost and time.
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Affiliation(s)
- Suleman Khan
- Department of Computer and Information Sciences, Northumbria University, Newcastle, Upon Tyne, NE1 8ST United Kingdom
| | - Muhammad Khurram Khan
- College of Computer & Information Sciences, King Saud University, Riyadh, 11653 Saudi Arabia
| | - Rizwan Khan
- Institute of Management Sciences (IM-Sciences), Peshawar, Pakistan
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13
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Bhatele KR, Jha A, Tiwari D, Bhatele M, Sharma S, Mithora MR, Singhal S. COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans. Cognit Comput 2022:1-38. [PMID: 36593991 PMCID: PMC9797382 DOI: 10.1007/s12559-022-10076-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 11/15/2022] [Indexed: 12/30/2022]
Abstract
This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.
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Affiliation(s)
| | - Anand Jha
- RJIT BSF Academy, Tekanpur, Gwalior India
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14
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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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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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16
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Naseem MT, Hussain T, Lee CS, Khan MA. Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning. SENSORS (BASEL, SWITZERLAND) 2022; 22:7977. [PMID: 36298328 PMCID: PMC9610066 DOI: 10.3390/s22207977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/17/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.
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Affiliation(s)
- Muhammad Tahir Naseem
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Tajmal Hussain
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
| | - Chan-Su Lee
- Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Muhammad Adnan Khan
- Riphah School of Computing & Applied Sciences (RSCI), Riphah International University, Lahore 55150, Pakistan
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Malik H, Anees T, Din M, Naeem A. CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:13855-13880. [PMID: 36157356 PMCID: PMC9485026 DOI: 10.1007/s11042-022-13843-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/30/2022] [Accepted: 09/06/2022] [Indexed: 05/27/2023]
Abstract
Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19's medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar's, and ANOVA tests also showed the robustness of the proposed model.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, University of Management and Technology, Lahore, 54000 Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, University of Management and Technology, Lahore, 54000 Pakistan
| | - Muizzud Din
- Department of Computer Science, Ghazi University, Dera Ghazi Khan, 32200 Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore, 54000 Pakistan
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Ukwuoma CC, Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muad AY, Addo D, Al-Antari MA. A Hybrid Explainable Ensemble Transformer Encoder for Pneumonia Identification from Chest X-ray Images. J Adv Res 2022:S2090-1232(22)00202-8. [PMID: 36084812 DOI: 10.1016/j.jare.2022.08.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 10/14/2022] Open
Abstract
INTRODUCTION Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. OBJECTIVES A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. METHODS The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification tasks. RESULTS The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble, multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. CONCLUSION The proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with individual, ensemble models, or even the latest models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.
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Affiliation(s)
- Chiagoziem C Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China; International Institute of Information Technology, Hyderabad, Telangana 500032, India; Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Olusola Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, China
| | - Abdullah Y Muad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore, India
| | - Daniel Addo
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Mugahed A Al-Antari
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Korea.
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Srivastava G, Chauhan A, Jangid M, Chaurasia S. CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images. Biomed Signal Process Control 2022; 78:103848. [PMID: 35694696 PMCID: PMC9174225 DOI: 10.1016/j.bspc.2022.103848] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/29/2022]
Abstract
The Coronavirus (COVID-19) pandemic has created havoc on humanity by causing millions of deaths and adverse physical and mental health effects. To prepare humankind for the fast and efficient detection of the virus and its variants shortly, COVID-19 detection using Artificial Intelligence and Computer-Aided Diagnosis has been the subject of several studies. To detect COVID-19, there are numerous publicly accessible datasets of Chest X-rays that the researchers have combined to solve the problem of inadequate data. The cause for concern here is that in combining two or more datasets, some of the images might be duplicates, so a curated dataset has been used in this study, taken from an author’s paper. This dataset consists of 1281 COVID-19, 3270 Normal X-rays, and 1656 viral-pneumonia infected Chest X-ray images. Dataset has been pre-processed and divided carefully to ensure that there are no duplicate images. A comparative study on many traditional pre-trained models was performed, analyzing top-performing models. Fine-tuned InceptionV3, Modified EfficientNet B0&B1 produced an accuracy of 99.78% on binary classification, i.e., covid-19 infected and normal Chest X-ray image. ResNetV2 had a classification accuracy of 97.90% for 3-class classification i.e., covid-19 infected, normal, and pneumonia. Furthermore, a trailblazing custom CNN-based model, CoviXNet, has been proposed consisting of 15 layers that take efficiency into account. The proposed model CoviXNet exhibited a 10-fold accuracy of 99.47% on binary classification and 96.61% on 3-class. CoviXNet has shown phenomenal performance with exceptional accuracy and minimum computational cost. We anticipate that this comparative study, along with the proposed model CoviXNet, can assist medical centers with the efficient real-life detection of Coronavirus.
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Affiliation(s)
- Gaurav Srivastava
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Aninditaa Chauhan
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Mahesh Jangid
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
| | - Sandeep Chaurasia
- Department of Computer Science and Engineering, Manipal University Jaipur, 303007, Rajasthan, India
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Badr M, Al-Otaibi S, Alturki N, Abir T. Detection of Heart Arrhythmia on Electrocardiogram using Artificial Neural Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1094830. [PMID: 36035826 PMCID: PMC9410968 DOI: 10.1155/2022/1094830] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 12/21/2022]
Abstract
The electrocardiogram, also known as an electrocardiogram (ECG), is considered to be one of the most significant sources of data regarding the structure and function of the heart. In order to obtain an electrocardiogram, the contractions and relaxations of the heart are first captured in the proper recording medium. Due to the fact that irregularities in the functioning of the heart are reflected in the ECG indications, it is possible to use these indications to diagnose cardiac issues. Arrhythmia is the medical term for the abnormalities that might occur in the regular functioning of the heart (rhythm disorder). Environmental and genetic variables can both play a role in the development of arrhythmias. Arrhythmias are reflected on the ECG sign, which depicts the same region regardless of where in the heart they occur; thus, they may be seen in ECG signals. This is how arrhythmias can be detected. Due to the time limits of this study, the ECG signals of individuals who were healthy, as well as those who suffered from arrhythmias were divided into 10-minute segments. The arithmetic mean approach is one of the fundamental statistical factors. It is used to construct the feature vectors of each received wave and interval, and these vectors offer information regarding arrhythmias in accordance with the agreed-upon temporal restrictions. In order to identify the heart arrhythmias, the obtained feature vectors are fed into a classifier that is based on a multilayer perceptron neural network. In conclusion, ROC analysis and contrast matrix are utilised in order to evaluate the overall correct classification result produced by the ECG-based classifier. Because of this, it has been demonstrated that the method that was recommended has high classification accuracy when attempting to diagnose arrhythmia based on ECG indications. This research makes use of a variety of diagnostic terminologies, including ECG signal, multilayer perceptron neural network, signal processing, disease diagnosis, and arrhythmia diagnosis.
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Affiliation(s)
- Malek Badr
- The University of Mashreq, Research Center, Baghdad, Iraq
- Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq
| | - Shaha Al-Otaibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Tanvir Abir
- Department of Business Administration, Faculty of Business and Entrepreneurship, Daffodil International University, Dhaka, Bangladesh
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22
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Asif S, Wenhui Y, Amjad K, Jin H, Tao Y, Jinhai S. Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning. EXPERT SYSTEMS 2022; 40:EXSY13099. [PMID: 35945966 PMCID: PMC9353436 DOI: 10.1111/exsy.13099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/26/2022] [Accepted: 06/30/2022] [Indexed: 05/27/2023]
Abstract
Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.
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Affiliation(s)
- Sohaib Asif
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Yi Wenhui
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
| | - Kamran Amjad
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
| | - Hou Jin
- School of Basic Medical ScienceXi'an Medical UniversityXi'anChina
| | - Yi Tao
- School of Computer Science and EngineeringXi'an Jiaotong UniversityXi'anChina
| | - Si Jinhai
- Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina
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Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7833516. [PMID: 35915789 PMCID: PMC9338857 DOI: 10.1155/2022/7833516] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022]
Abstract
X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.
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Sri Kavya N, Shilpa T, Veeranjaneyulu N, Divya Priya D. Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks. MATERIALS TODAY. PROCEEDINGS 2022; 64:737-743. [PMID: 35607444 PMCID: PMC9117408 DOI: 10.1016/j.matpr.2022.05.199] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
With the current COVID19 pandemic, we have to weigh human life, prosperity, and value, while implicitly acknowledging that controlling case spread and mortality is a challenge. Identifying COVID19-infected patients and disconnecting them to avoid COVID transmission is one of the most difficult tasks for clinicians. As a result, figuring out who infected with covid19 is crucial. COVID19 is identified using a 4-6-hour reverse transcription-polymerase chain reaction (RT-PCR). Another way to detect Coronavirus early in the disease process is by using chest X-rays (CXR).We extracted characteristics from chest X-ray images using VGG16 and ResNet50 deep learning algorithms, then classified them into three groups: viral pneumonia, normal, and COVID19. We ran 15,153 images through the models to see how accurate they were in real-world situations. For detecting COVID19 cases, the VGG16 model has an average accuracy of 89.34 %, whereas ResNet50 has an accuracy of 91.39 %. When utilizing deep learning to identify COVID19, however, a larger dataset is necessary. It has the desired effect of detecting situations accurately.
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Affiliation(s)
- Nallamothu Sri Kavya
- Department of IT, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India
| | - Thotapalli Shilpa
- Department of IT, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India
| | - N Veeranjaneyulu
- Department of IT, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur, Andhra Pradesh, India
| | - D Divya Priya
- Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India
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Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN COMPUTER SCIENCE 2022; 3:286. [PMID: 35578678 PMCID: PMC9096341 DOI: 10.1007/s42979-022-01184-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 04/30/2022] [Indexed: 12/12/2022]
Abstract
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.
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Affiliation(s)
- Yassine Meraihi
- LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, 35000 Boumerdes, Algeria
| | - Asma Benmessaoud Gabis
- Ecole nationale Supérieure d'Informatique, Laboratoire des Méthodes de Conception des Systèmes, BP 68 M, 16309 Oued-Smar, Alger Algeria
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.,Yonsei Frontier Lab, Yonsei University, Seoul, Korea
| | - Amar Ramdane-Cherif
- LISV Laboratory, University of Versailles St-Quentin-en-Yvelines, 10-12 Avenue of Europe, 78140 Velizy, France
| | - Fawaz E Alsaadi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Sevli O. A deep learning-based approach for diagnosing COVID-19 on chest x-ray images, and a test study with clinical experts. Comput Intell 2022; 38:COIN12526. [PMID: 35941907 PMCID: PMC9348396 DOI: 10.1111/coin.12526] [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: 10/15/2021] [Revised: 03/06/2022] [Accepted: 04/05/2022] [Indexed: 01/20/2023]
Abstract
Pneumonia is among the common symptoms of the virus that causes COVID-19, which has turned into a worldwide pandemic. It is possible to diagnose pneumonia by examining chest radiographs. Chest x-ray (CXR) is a fast, low-cost, and practical method widely used in this field. The fact that different pathogens other than COVID-19 also cause pneumonia and the radiographic images of all are similar make it difficult to detect the source of the disease. In this study, automatic detection of COVID-19 cases over CXR images was tried to be performed using convolutional neural network (CNN), a deep learning technique. Classifications were carried out using six different architectures on the dataset consisting of 15,153 images of three different types: healthy, COVID-19, and other viral-induced pneumonia. In the classifications performed with five different state-of-art models, ResNet18, GoogLeNet, AlexNet, VGG16, and DenseNet161, and a minimal CNN architecture specific to this study, the most successful result was obtained with the ResNet18 architecture as 99.25% accuracy. Although the minimal CNN model developed for this study has a simpler structure, it was observed that it has a success to compete with more complex models. The performances of the models used in this study were compared with similar studies in the literature and it was revealed that they generally achieved higher success. The model with the highest success was transformed into a test application, tested by 10 volunteer clinicians, and it was concluded that it provides 99.06% accuracy in practical use. This result reveals that the conducted study can play the role of a successful decision support system for experts.
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Affiliation(s)
- Onur Sevli
- Faculty of Engineering and Architecture, Computer Engineering DepartmentBurdur Mehmet Akif Ersoy UniversityBurdurTurkey
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Hassan F, Albahli S, Javed A, Irtaza A. A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19. Front Public Health 2022; 10:805086. [PMID: 35602122 PMCID: PMC9120631 DOI: 10.3389/fpubh.2022.805086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Covid-19 has become a pandemic that affects lots of individuals daily, worldwide, and, particularly, the widespread disruption in numerous countries, namely, the US, Italy, India, Saudi Arabia. The timely detection of this infectious disease is mandatory to prevent the quick spread globally and locally. Moreover, the timely detection of COVID-19 in the coming time is significant to well cope with the disease control by Governments. The common symptoms of COVID are fever as well as dry cough, which is similar to the normal flu. The disease is devastating and spreads quickly, which affects individuals of all ages, particularly, aged people and those with feeble immune systems. There is a standard method employed to detect the COVID, namely, the real-time polymerase chain reaction (RT-PCR) test. But this method has shortcomings, i.e., it takes a long time and generates maximum false-positive cases. Consequently, we necessitate to propose a robust framework for the detection as well as for the estimation of COVID cases globally. To achieve the above goals, we proposed a novel technique to analyze, predict, and detect the COVID-19 infection. We made dependable estimates on significant pandemic parameters and made predictions of infection as well as potential washout time frames for numerous countries globally. We used a publicly available dataset composed by Johns Hopkins Center for estimation, analysis, and predictions of COVID cases during the time period of 21 April 2020 to 27 June 2020. We employed a simple circulation for fast as well as simple estimates of the COVID model and estimated the parameters of the Gaussian curve, utilizing a parameter, namely, the least-square parameter curve fitting for numerous countries in distinct areas. Forecasts of COVID depend upon the potential results of Gaussian time evolution with a central limit theorem of data the Covid prediction to be justified. For gaussian distribution, the parameters, namely, extreme time and thickness are regulated using a statistical Y2 fit for the aim of doubling times after 21 April 2020. Moreover, for the detection of COVID-19, we also proposed a novel technique, employing the two features, namely, Histogram of Oriented Gradients and Scale Invariant Feature Transform. We also designed a CNN-based architecture named COVIDDetectorNet for classification purposes. We fed the extracted features into the proposed COVIDDetectorNet to detect COVID-19, viral pneumonia, and other lung infections. Our method obtained an accuracy of 96.51, 92.62, and 86.53% for two, three, and four classes, respectively. Experimental outcomes illustrate that our method is reliable to be employed for the forecast and detection of COVID-19 disease.
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Affiliation(s)
- Farman Hassan
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
| | - Saleh Albahli
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
- *Correspondence: Saleh Albahli
| | - Ali Javed
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
- Department of Computer Science and Engineering, Oakland University, Detroit, MI, United States
| | - Aun Irtaza
- Department of Computer and Electrical Engineering, University of Michigan, Dearborn, MI, United States
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Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis. Inf Sci (N Y) 2022; 592:389-401. [DOI: 10.1016/j.ins.2022.01.062] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 12/12/2022]
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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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30
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Wang D, Willis DR, Yih Y. The pneumonia severity index: Assessment and comparison to popular machine learning classifiers. Int J Med Inform 2022; 163:104778. [PMID: 35487075 DOI: 10.1016/j.ijmedinf.2022.104778] [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: 01/13/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. METHODS In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). RESULTS Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). DISCUSSION Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.
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Affiliation(s)
- Dawei Wang
- School of Industrial Engineering, Purdue University, 315 Grant St, West Lafayette, IN 47907, USA.
| | - Deanna R Willis
- Indiana University School of Medicine, Department of Family Medicine, 1110 W. Michigan St, LO 200, Indianapolis, IN 46202, USA
| | - Yuehwern Yih
- School of Industrial Engineering, Purdue University, 315 Grant St, West Lafayette, IN 47907, USA
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31
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Anand S, Sharma V, Pourush R, Jaiswal S. A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19. Ann Med Surg (Lond) 2022; 76:103519. [PMID: 35401978 PMCID: PMC8975609 DOI: 10.1016/j.amsu.2022.103519] [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: 02/01/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
The novel coronavirus, renamed SARS-CoV-2 and most commonly referred to as COVID-19, has infected nearly 44.83 million people in 224 countries and has been designated SARS-CoV-2. In this study, we used ‘web of Science’, ‘Scopus’ and ‘goggle scholar’ with the keywords of “SARS-CoV-2 detection” or “coronavirus 2019 detection” or “COVID 2019 detection” or “COVID 19 detection” “corona virus techniques for detection of COVID-19”, “audio techniques for detection of COVID-19”, “speech techniques for detection of COVID-19”, for period of 2019–2021. Some COVID-19 instances have an impact on speech production, which suggests that researchers should look for signs of disease detection in speech utilising audio and speech recognition signals from humans to better understand the condition. It is presented in this review that an overview of human audio signals is presented using an AI (Artificial Intelligence) model to diagnose, spread awareness, and monitor COVID-19, employing bio and non-obtrusive signals that communicated human speech and non-speech audio information is presented. Development of accurate and rapid screening techniques that permit testing at a reasonable cost is critical in the current COVID-19 pandemic crisis, according to the World Health Organization. In this context, certain existing investigations have shown potential in the detection of COVID 19 diagnostic signals from relevant auditory noises, which is a promising development. According to authors, it is not a single “perfect” COVID-19 test that is required, but rather a combination of rapid and affordable tests, non-clinic pre-screening tools, and tools from a variety of supply chains and technologies that will allow us to safely return to our normal lives while we await the completion of the hassle free COVID-19 vaccination process for all ages. This review was able to gather information on biomedical signal processing in the detection of speech, coughing sounds, and breathing signals for the purpose of diagnosing and screening the COVID-19 virus. This is a comprehensive review of published work for the detection of COVID-19. Previously conducted studies on audio, voice, cough sound, breathing and signal processing methods in order to address COVID-19-related health conditions. Analyzing and diagnosing COVID-19 using audio, speech and Signal Processing. Diagnosing and Screening of COVID-19 are studied using Machine Learning, Artificial Intelligence and Deep Learning. An overall these Signal Processing, Machine Learning, Artificial Intelligence and Deep Learning techniques were seen to have satisfactory results for the detection of COVID-19.
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Affiliation(s)
- Satyajit Anand
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Vikrant Sharma
- Mechanical Engineering, Mody University of Science and Technology, India
| | - Rajeev Pourush
- Electronics and Communication Engineering, Mody University of Science and Technology, India
| | - Sandeep Jaiswal
- Biomedical Engineering, Mody University of Science and Technology, India
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32
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022. [PMID: 35348909 DOI: 10.1101/2021.04.06.21254997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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33
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022; 46:23. [PMID: 35348909 PMCID: PMC8960704 DOI: 10.1007/s10916-022-01807-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
AbstractMany previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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Kumar R, Arora R, Bansal V, Sahayasheela VJ, Buckchash H, Imran J, Narayanan N, Pandian GN, Raman B. Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:27631-27655. [PMID: 35368858 PMCID: PMC8958819 DOI: 10.1007/s11042-022-12500-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/18/2021] [Accepted: 01/25/2022] [Indexed: 06/12/2023]
Abstract
COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.
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Affiliation(s)
- Rahul Kumar
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
- Department of Computer Engineering & Applications, GLA University, Mathura, Uttar Pradesh India
| | - Ridhi Arora
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Vipul Bansal
- Department of Mechanical & Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Vinodh J Sahayasheela
- Institute of Integrated Cell Material Sciences (WPI-iCeMS), Kyoto University of Advanced Study, Kyoto, Japan
| | - Himanshu Buckchash
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Javed Imran
- School of Computer Science, University of Petroleum & Energy Studies (UPES), Dehradun, India
| | - Narayanan Narayanan
- Centre for Research and Graduate Studies, University of CyberJaya, Sepang, Malaysia
| | - Ganesh N Pandian
- Institute of Integrated Cell Material Sciences (WPI-iCeMS), Kyoto University of Advanced Study, Kyoto, Japan
| | - Balasubramanian Raman
- Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India
- Centre for Research and Graduate Studies, University of CyberJaya, Sepang, Malaysia
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35
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Classification of COVID-19 and Influenza Patients Using Deep Learning. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:8549707. [PMID: 35280712 PMCID: PMC8884121 DOI: 10.1155/2022/8549707] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/26/2022] [Indexed: 12/13/2022]
Abstract
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
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Nayak J, Naik B, Dinesh P, Vakula K, Dash PB, Pelusi D. Significance of deep learning for Covid-19: state-of-the-art review. RESEARCH ON BIOMEDICAL ENGINEERING 2022. [PMCID: PMC7980106 DOI: 10.1007/s42600-021-00135-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose The appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computer-based health analysis is necessary for gaining clinicians’ expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists. Method In this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NCOV-19. Conclusion The main aim behind this research work is to inspire the research community and to innovate novel research using deep learning. Moreover, the outcome of this detailed structured review on the impact of deep learning in covid-19 analysis will be helpful for further investigations on various modalities of diseases detection, prevention and finding novel solutions.
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Affiliation(s)
- Janmenjoy Nayak
- Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), K Kotturu, Tekkali, AP 532201 India
| | - Bighnaraj Naik
- Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Paidi Dinesh
- Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP 532402 India
| | - Kanithi Vakula
- Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, AP 532402 India
| | - Pandit Byomakesha Dash
- Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Odisha 768018 India
| | - Danilo Pelusi
- Faculty of Communication Sciences, University of Teramo, Coste Sant', Agostino Campus, Teramo, Italy
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Yao HY, Wan WG, Li X. A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022; 2022:10. [PMID: 35194421 PMCID: PMC8830991 DOI: 10.1186/s13634-022-00842-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/27/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area is difficult to distinguish manually annotation, the segmentation results are time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provide lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnosis and segmentation show superior performance over state-of-the-art methods.
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Affiliation(s)
- Hai-yan Yao
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
- Anyang Institute of Technology, Anyang, China
| | - Wang-gen Wan
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiang Li
- School of Communication and Information Engineering, Shanghai University, Shanghai, China
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38
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Ali Ahmed SA, Yavuz MC, Şen MU, Gülşen F, Tutar O, Korkmazer B, Samancı C, Şirolu S, Hamid R, Eryürekli AE, Mammadov T, Yanikoglu B. Comparison and Ensemble of 2D and 3D Approaches for COVID-19 Detection in CT Images. Neurocomputing 2022; 488:457-469. [PMID: 35345875 PMCID: PMC8942080 DOI: 10.1016/j.neucom.2022.02.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/06/2021] [Accepted: 02/03/2022] [Indexed: 12/31/2022]
Abstract
Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpaşa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.
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Affiliation(s)
- Sara Atito Ali Ahmed
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, U.K.7XH, UK
| | - Mehmet Can Yavuz
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Mehmet Umut Şen
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Fatih Gülşen
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Onur Tutar
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Bora Korkmazer
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Cesur Samancı
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Sabri Şirolu
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Rauf Hamid
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Ali Ergun Eryürekli
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Toghrul Mammadov
- Istanbul University-Cerrahpaşa, Cerrahpaşa Faculty of Medicine, Istanbul 34096, Turkey
| | - Berrin Yanikoglu
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
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COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN. Diagnostics (Basel) 2022; 12:diagnostics12020267. [PMID: 35204358 PMCID: PMC8871483 DOI: 10.3390/diagnostics12020267] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/07/2022] [Accepted: 01/16/2022] [Indexed: 02/01/2023] Open
Abstract
COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.
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40
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Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs. Healthcare (Basel) 2022; 10:healthcare10010175. [PMID: 35052339 PMCID: PMC8775598 DOI: 10.3390/healthcare10010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 01/09/2022] [Accepted: 01/14/2022] [Indexed: 11/25/2022] Open
Abstract
(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the “live” dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.
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Attallah O. ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration. Comput Biol Med 2022; 142:105210. [PMID: 35026574 PMCID: PMC8730786 DOI: 10.1016/j.compbiomed.2022.105210] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 12/29/2022]
Abstract
The accurate and speedy detection of COVID-19 is essential to avert the fast propagation of the virus, alleviate lockdown constraints and diminish the burden on health organizations. Currently, the methods used to diagnose COVID-19 have several limitations, thus new techniques need to be investigated to improve the diagnosis and overcome these limitations. Taking into consideration the great benefits of electrocardiogram (ECG) applications, this paper proposes a new pipeline called ECG-BiCoNet to investigate the potential of using ECG data for diagnosing COVID-19. ECG-BiCoNet employs five deep learning models of distinct structural design. ECG-BiCoNet extracts two levels of features from two different layers of each deep learning technique. Features mined from higher layers are fused using discrete wavelet transform and then integrated with lower-layers features. Afterward, a feature selection approach is utilized. Finally, an ensemble classification system is built to merge predictions of three machine learning classifiers. ECG-BiCoNet accomplishes two classification categories, binary and multiclass. The results of ECG-BiCoNet present a promising COVID-19 performance with an accuracy of 98.8% and 91.73% for binary and multiclass classification categories. These results verify that ECG data may be used to diagnose COVID-19 which can help clinicians in the automatic diagnosis and overcome limitations of manual diagnosis.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 1029, Egypt.
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42
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Peng Y, Zhang Z, Tu H, Li X. Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network. Front Med (Lausanne) 2022; 8:755309. [PMID: 35047520 PMCID: PMC8761973 DOI: 10.3389/fmed.2021.755309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 11/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The novel coronavirus disease 2019 (COVID-19) has been spread widely in the world, causing a huge threat to the living environment of people. Objective: Under CT imaging, the structure features of COVID-19 lesions are complicated and varied greatly in different cases. To accurately locate COVID-19 lesions and assist doctors to make the best diagnosis and treatment plan, a deep-supervised ensemble learning network is presented for COVID-19 lesion segmentation in CT images. Methods: Since a large number of COVID-19 CT images and the corresponding lesion annotations are difficult to obtain, a transfer learning strategy is employed to make up for the shortcoming and alleviate the overfitting problem. Based on the reality that traditional single deep learning framework is difficult to extract complicated and varied COVID-19 lesion features effectively that may cause some lesions to be undetected. To overcome the problem, a deep-supervised ensemble learning network is presented to combine with local and global features for COVID-19 lesion segmentation. Results: The performance of the proposed method was validated in experiments with a publicly available dataset. Compared with manual annotations, the proposed method acquired a high intersection over union (IoU) of 0.7279 and a low Hausdorff distance (H) of 92.4604. Conclusion: A deep-supervised ensemble learning network was presented for coronavirus pneumonia lesion segmentation in CT images. The effectiveness of the proposed method was verified by visual inspection and quantitative evaluation. Experimental results indicated that the proposed method has a good performance in COVID-19 lesion segmentation.
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Affiliation(s)
- Yuanyuan Peng
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Zixu Zhang
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China
| | - Hongbin Tu
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China
- Technique Center, Hunan Great Wall Technology Information Co. Ltd., Changsha, China
| | - Xiong Li
- School of Software, East China Jiaotong University, Nanchang, China
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43
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Chahar S, Roy PK. COVID-19: A Comprehensive Review of Learning Models. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2022; 29:1915-1940. [PMID: 34566404 PMCID: PMC8449694 DOI: 10.1007/s11831-021-09641-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/31/2021] [Indexed: 05/17/2023]
Abstract
Coronavirus disease is communicable and inhibits the infected person's immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.
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Affiliation(s)
- Shivam Chahar
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TN India
| | - Pradeep Kumar Roy
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, Gujarat India
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44
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Trivedi M, Gupta A. A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:5515-5536. [PMID: 34975283 PMCID: PMC8711865 DOI: 10.1007/s11042-021-11807-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 08/26/2021] [Accepted: 12/14/2021] [Indexed: 05/07/2023]
Abstract
Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.
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Affiliation(s)
- Megha Trivedi
- School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, Jammu and Kashmir 182 320 India
| | - Abhishek Gupta
- School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Kakryal, Katra, Jammu and Kashmir 182 320 India
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45
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Appasami G, Nickolas S. A deep learning-based COVID-19 classification from chest X-ray image: case study. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3767-3777. [PMID: 35996535 PMCID: PMC9386662 DOI: 10.1140/epjs/s11734-022-00647-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/26/2022] [Indexed: 05/02/2023]
Abstract
The novel corona virus disease (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and more than 6 millions of people died in last 2 years. Early detection of COVID-19 can mitigate and control its spread. Reverse transcription polymerase chain reaction (RT-CPR), Chest X-ray (CXR) scan, and Computerized Tomography (CT) scan are used to identify the COVID-19. Chest X-ray image analysis is relatively time efficient than compared with RT-CPR and CT scan. Its cost-effectiveness make it a good choice for COVID-19 Classification. We propose a deep learning based Convolutional Neural Network model for detection of COVID-19 from CXR. Chest X-ray images are collected from various sources dataset for training with augmentation and evaluating our model, which is widely used for COVID-19 detection and diagnosis. A Deep Convolutional neural network (CNN) based model for analysis of COVID-19 with data augmentation is proposed, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to help the physicians to assist the diagnostic process among high workload conditions. The overall accuracy of 93 percent for COVID-19 Classification is achieved by choosing best optimizer.
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Affiliation(s)
- G. Appasami
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamilnadu India
| | - S. Nickolas
- National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamilnadu India
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46
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de Moura J, Novo J, Ortega M. Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images. Appl Soft Comput 2021; 115:108190. [PMID: 34899109 PMCID: PMC8645263 DOI: 10.1016/j.asoc.2021.108190] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 10/24/2021] [Accepted: 11/22/2021] [Indexed: 12/24/2022]
Abstract
Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.
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Affiliation(s)
- Joaquim de Moura
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071, A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071, A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, 15071, A Coruña, Spain.,Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, 15006, A Coruña, Spain
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47
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Haq AU, Li JP, Ahmad S, Khan S, Alshara MA, Alotaibi RM. Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare. SENSORS (BASEL, SWITZERLAND) 2021; 21:8219. [PMID: 34960313 PMCID: PMC8707954 DOI: 10.3390/s21248219] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/25/2021] [Accepted: 11/30/2021] [Indexed: 01/15/2023]
Abstract
COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.
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Affiliation(s)
- Amin Ul Haq
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jian Ping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Sultan Ahmad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
| | - Mohammed Ali Alshara
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
| | - Reemiah Muneer Alotaibi
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (M.A.A.); (R.M.A.)
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48
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Bhardwaj P, Kaur A. A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021; 31:1775-1791. [PMID: 34518739 PMCID: PMC8426690 DOI: 10.1002/ima.22627] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/06/2021] [Accepted: 06/27/2021] [Indexed: 05/03/2023]
Abstract
With the exponential growth of COVID-19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID-19, 2022 pneumonia, and 5863 normal chest X-ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre-processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.
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Affiliation(s)
| | - Amanpreet Kaur
- Electronics and CommunicationThapar UniversityPatialaIndia
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An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques. BIOMED RESEARCH INTERNATIONAL 2021; 2021:1896762. [PMID: 34782860 PMCID: PMC8590595 DOI: 10.1155/2021/1896762] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/29/2021] [Accepted: 10/16/2021] [Indexed: 11/17/2022]
Abstract
The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.
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Bashar A, Latif G, Ben Brahim G, Mohammad N, Alghazo J. COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques. Diagnostics (Basel) 2021; 11:1972. [PMID: 34829319 PMCID: PMC8625739 DOI: 10.3390/diagnostics11111972] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/24/2022] Open
Abstract
It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.
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Affiliation(s)
- Abul Bashar
- Department of Computer Engineering, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
| | - Ghazanfar Latif
- Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H2B1, Canada;
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Ghassen Ben Brahim
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Nazeeruddin Mohammad
- Cybersecurity Center, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia;
| | - Jaafar Alghazo
- Department of Electrical and Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA;
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