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Ahmed KB, Goldgof GM, Paul R, Goldgof DB, Hall LO. Discovery of a Generalization Gap of Convolutional Neural Networks on COVID-19 X-Rays Classification. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:72970-72979. [PMID: 34178559 PMCID: PMC8224464 DOI: 10.1109/access.2021.3079716] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 05/04/2021] [Indexed: 05/02/2023]
Abstract
A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models have high accuracy on seen sources, but poor accuracy on unseen sources. The reason for the disparity is that the convolutional neural network model, which learns features, can focus on differences in X-ray machines or in positioning within the machines, for example. Any feature that a person would clearly rule out is called a confounding feature. Some of the models were trained on COVID-19 image data taken from publications, which may be different than raw images. Some data sets were of pediatric cases with pneumonia where COVID-19 chest X-rays are almost exclusively from adults, so lung size becomes a spurious feature that can be exploited. In this work, we have eliminated many confounding features by working with as close to raw data as possible. Still, deep learned models may leverage source specific confounders to differentiate COVID-19 from pneumonia preventing generalizing to new data sources (i.e. external sites). Our models have achieved an AUC of 1.00 on seen data sources but in the worst case only scored an AUC of 0.38 on unseen ones. This indicates that such models need further assessment/development before they can be broadly clinically deployed. An example of fine-tuning to improve performance at a new site is given.
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Affiliation(s)
- Kaoutar Ben Ahmed
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
| | - Gregory M. Goldgof
- Department of Laboratory MedicineThe University of CaliforniaSan FranciscoCA94143USA
| | - Rahul Paul
- Department of Radiation OncologyMassachusetts General HospitalBostonMA02115USA
- Department of Radiation OncologyHarvard Medical SchoolBostonMA02115USA
| | - Dmitry B. Goldgof
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
| | - Lawrence O. Hall
- Department of Computer Science and EngineeringUniversity of South FloridaTampaFL33620USA
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102
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Yang Y, Li Q, Guo Y, Liu Y, Li X, Guo J, Li W, Cheng L, Chen H, Kang Y. Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4193-4211. [PMID: 34198432 DOI: 10.3934/mbe.2021210] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Our paper proposes a method to measure lung parenchyma parameters from pulmonary window computed tomography images based on ResU-Net model including the CT value, the density, the lung volume, and the surface area of the lungs of healthy rats, to help promote the quantitative analysis of lung parenchyma parameters of rats in medical respiratory researches. Through the analysis of the lung parenchyma parameters of the control group and the treatment group, the law of change among the lung parenchyma parameters is given in our paper. After comparing and analyzing the lung parenchyma parameter CT value and the density of the two groups, it is discovered that the lung parenchyma parameter CT value and the density significantly increase in the treatment group which is after continuously inhaling the nebulization of contrast agents. The change of the lung volume with the surface area in both two groups conforms to the law of lung changes during breathing. The relationship between the lung volume and the CT value or the density is analyzed and it is concluded that the lung volume is negatively correlated with the CT value or the density.
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Qiang Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Yang Liu
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Xian Li
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Jiaqi Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Wei Li
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Lei Cheng
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Huai Chen
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
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103
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Li J, Wang Y, Wang S, Wang J, Liu J, Jin Q, Sun L. Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images. IEEE J Biomed Health Inform 2021; 25:1336-1346. [PMID: 33560995 PMCID: PMC8545167 DOI: 10.1109/jbhi.2021.3058293] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.
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104
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Agrawal T, Choudhary P. FocusCovid: automated COVID-19 detection using deep learning with chest X-ray images. EVOLVING SYSTEMS 2021; 13:519-533. [PMID: 38624806 PMCID: PMC8106902 DOI: 10.1007/s12530-021-09385-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/29/2021] [Indexed: 12/28/2022]
Abstract
COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician.
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Affiliation(s)
- Tarun Agrawal
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India
| | - Prakash Choudhary
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India
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105
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Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 2021; 24:1207-1220. [PMID: 33994847 PMCID: PMC8106971 DOI: 10.1007/s10044-021-00984-y] [Citation(s) in RCA: 511] [Impact Index Per Article: 170.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 04/29/2021] [Indexed: 01/09/2023]
Abstract
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.
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Affiliation(s)
- Ali Narin
- Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
| | - Ceren Kaya
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
| | - Ziynet Pamuk
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
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106
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Kugunavar S, Prabhakar CJ. Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic. Vis Comput Ind Biomed Art 2021; 4:12. [PMID: 33950399 PMCID: PMC8097673 DOI: 10.1186/s42492-021-00078-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 04/19/2021] [Indexed: 12/21/2022] Open
Abstract
A neural network is one of the current trends in deep learning, which is increasingly gaining attention owing to its contribution in transforming the different facets of human life. It also paves a way to approach the current crisis caused by the coronavirus disease (COVID-19) from all scientific directions. Convolutional neural network (CNN), a type of neural network, is extensively applied in the medical field, and is particularly useful in the current COVID-19 pandemic. In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients. The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images. The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.
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Affiliation(s)
- Sneha Kugunavar
- Department of Computer Science, Kuvempu University, Shimoga, Karnataka, 577451, India.
| | - C J Prabhakar
- Department of Computer Science, Kuvempu University, Shimoga, Karnataka, 577451, India
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107
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Poly TN, Islam MM, Li YCJ, Alsinglawi B, Hsu MH, Jian WS, Yang HC. Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis. JMIR Med Inform 2021; 9:e21394. [PMID: 33764884 PMCID: PMC8086786 DOI: 10.2196/21394] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/04/2020] [Accepted: 03/21/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. OBJECTIVE The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. METHODS A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms "COVID-19," or "coronavirus," or "SARS-CoV-2," or "novel corona," or "2019-ncov," and "deep learning," or "artificial intelligence," or "automatic detection." Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. RESULTS A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95% CI 0.94-0.95) and 0.96 (95% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95% CI 12.83-28.19), 0.06 (95% CI 0.04-0.10), and 368.07 (95% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95% CI 0.92-0.94) and 0.95 (95% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. CONCLUSIONS Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.
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Affiliation(s)
- Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Md Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan
| | - Belal Alsinglawi
- School of Computer, Data, and Mathematical Science, Western Sydney University, Sydney, Australia
| | - Min-Huei Hsu
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wen Shan Jian
- School of Health Care Administration, Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
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108
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Montazeri M, ZahediNasab R, Farahani A, Mohseni H, Ghasemian F. Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review. JMIR Med Inform 2021; 9:e25181. [PMID: 33735095 PMCID: PMC8074953 DOI: 10.2196/25181] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 12/31/2020] [Accepted: 01/16/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images. OBJECTIVE The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care. METHODS A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19-related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. RESULTS Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non-neural network-based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting. CONCLUSIONS Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.
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Affiliation(s)
- Mahdieh Montazeri
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Roxana ZahediNasab
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Ali Farahani
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hadis Mohseni
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fahimeh Ghasemian
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
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109
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Albahli S, Rauf HT, Algosaibi A, Balas VE. AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays. PeerJ Comput Sci 2021; 7:e495. [PMID: 33977135 PMCID: PMC8064140 DOI: 10.7717/peerj-cs.495] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 03/27/2021] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.
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Affiliation(s)
- Saleh Albahli
- Department of Information Technology, College of Computer Science, Qassim University, Buraydah, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, stoke on Trent, United Kingdom
| | | | - Valentina Emilia Balas
- Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania
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110
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da Silva CC, de Lima CL, da Silva ACG, Silva EL, Marques GS, de Araújo LJB, Albuquerque Júnior LA, de Souza SBJ, de Santana MA, Gomes JC, Barbosa VADF, Musah A, Kostkova P, dos Santos WP, da Silva Filho AG. Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting. Front Public Health 2021; 9:641253. [PMID: 33898377 PMCID: PMC8060573 DOI: 10.3389/fpubh.2021.641253] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 03/11/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post-the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.
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Affiliation(s)
| | - Clarisse Lins de Lima
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Recife, Brazil
| | | | - Eduardo Luiz Silva
- Center for Informatics, Federal University of Pernambuco, Recife, Brazil
| | | | | | | | | | - Maíra Araújo de Santana
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Recife, Brazil
| | - Juliana Carneiro Gomes
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Recife, Brazil
| | - Valter Augusto de Freitas Barbosa
- Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Brazil
- Academic Unit of Serra Talhada, Rural Federal University of Pernambuco, Serra Talhada, Brazil
| | - Anwar Musah
- Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Patty Kostkova
- Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
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111
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Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Front Cardiovasc Med 2021; 8:638011. [PMID: 33842563 PMCID: PMC8027078 DOI: 10.3389/fcvm.2021.638011] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/23/2021] [Indexed: 12/15/2022] Open
Abstract
Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohadeseh Nadimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
| | - Azadeh Ghalyanchi-Langeroudi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
| | - Mohammad Taheri
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soudeh Ghafouri-Fard
- Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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112
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Salvatore C, Interlenghi M, Monti CB, Ippolito D, Capra D, Cozzi A, Schiaffino S, Polidori A, Gandola D, Alì M, Castiglioni I, Messa C, Sardanelli F. Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia. Diagnostics (Basel) 2021; 11:530. [PMID: 33809625 PMCID: PMC8000736 DOI: 10.3390/diagnostics11030530] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 02/05/2023] Open
Abstract
We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.
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Affiliation(s)
- Christian Salvatore
- Department of Science, Technology, and Society, Scuola Universitaria IUSS, Istituto Universitario di Studi Superiori, Piazza della Vittoria 15, 27100 Pavia, Italy;
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Matteo Interlenghi
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Caterina B. Monti
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Davide Ippolito
- Department of Radiology, ASST Monza—Ospedale San Gerardo, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.)
| | - Davide Capra
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
| | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Italy;
| | - Annalisa Polidori
- DeepTrace Technologies S.R.L., via Conservatorio 17, 20122 Milano, Italy; (M.I.); (A.P.)
| | - Davide Gandola
- Department of Radiology, ASST Monza—Ospedale San Gerardo, Via Pergolesi 33, 20900 Monza, Italy; (D.I.); (D.G.)
| | - Marco Alì
- Department of Diagnostic Imaging and Stereotactic Radiosurgery, C.D.I. Centro Diagnostico Italiano S.p.A., Via Saint Bon 20, 20147 Milano, Italy;
| | - Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy
- Institute of Biomedical Imaging and Physiology, Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, 20090 Segrate, Italy
| | - Cristina Messa
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy;
- Fondazione Tecnomed, Università degli Studi di Milano-Bicocca, Palazzina Ciclotrone—Via Pergolesi 33, 20900 Monza, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133 Milano, Italy; (C.B.M.); (D.C.); (A.C.); (F.S.)
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Italy;
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113
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Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6677314. [PMID: 33747419 PMCID: PMC7958142 DOI: 10.1155/2021/6677314] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/08/2021] [Accepted: 02/11/2021] [Indexed: 12/17/2022]
Abstract
Introduction The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.
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Affiliation(s)
- Mustafa Ghaderzadeh
- Student Research Committee, Department and Faculty of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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114
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Rahman S, Sarker S, Miraj MAA, Nihal RA, Nadimul Haque AKM, Noman AA. Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis. Cognit Comput 2021:1-30. [PMID: 33680209 PMCID: PMC7921610 DOI: 10.1007/s12559-020-09779-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/08/2020] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.
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Affiliation(s)
- Sejuti Rahman
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Sujan Sarker
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Md Abdullah Al Miraj
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Ragib Amin Nihal
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - A. K. M. Nadimul Haque
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Abdullah Al Noman
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
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115
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Elgendi M, Nasir MU, Tang Q, Smith D, Grenier JP, Batte C, Spieler B, Leslie WD, Menon C, Fletcher RR, Howard N, Ward R, Parker W, Nicolaou S. The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective. Front Med (Lausanne) 2021; 8:629134. [PMID: 33732718 PMCID: PMC7956964 DOI: 10.3389/fmed.2021.629134] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 01/29/2021] [Indexed: 01/07/2023] Open
Abstract
Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with aχ McNema r ' s statistic 2 = 163 . 2 and a p-value of 2.23 × 10-37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.
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Affiliation(s)
- Mohamed Elgendi
- Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- School of Mechatronic Systems Engineering, Simon Fraser University, Burnaby, BC, Canada
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Muhammad Umer Nasir
- Department of Emergency and Trauma Radiology, Vancouver General Hospital, Vancouver, BC, Canada
| | - Qunfeng Tang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - David Smith
- Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | - John-Paul Grenier
- Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | - Catherine Batte
- Department of Physics & Astronomy, Louisiana State University, Baton Rouge, LA, United States
| | - Bradley Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, New Orleans, LA, United States
| | | | - Carlo Menon
- School of Mechatronic Systems Engineering, Simon Fraser University, Burnaby, BC, Canada
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | | | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - William Parker
- Department of Emergency and Trauma Radiology, Vancouver General Hospital, Vancouver, BC, Canada
| | - Savvas Nicolaou
- Department of Emergency and Trauma Radiology, Vancouver General Hospital, Vancouver, BC, Canada
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
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116
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Pathan S, Siddalingaswamy PC, Ali T. Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture. Appl Soft Comput 2021; 104:107238. [PMID: 33649705 PMCID: PMC7903912 DOI: 10.1016/j.asoc.2021.107238] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/21/2021] [Accepted: 02/21/2021] [Indexed: 12/13/2022]
Abstract
The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.
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Affiliation(s)
- Sameena Pathan
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - P C Siddalingaswamy
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Tanweer Ali
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
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117
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Tan W, Liu P, Li X, Liu Y, Zhou Q, Chen C, Gong Z, Yin X, Zhang Y. Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network. Health Inf Sci Syst 2021; 9:10. [PMID: 33643612 PMCID: PMC7896179 DOI: 10.1007/s13755-021-00140-0] [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: 06/13/2020] [Accepted: 01/16/2021] [Indexed: 12/13/2022] Open
Abstract
The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19's artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.
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Affiliation(s)
- Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, 110189 China
| | - Pan Liu
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, 110189 China
| | - Xiaoshuo Li
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, 110189 China
| | - Yao Liu
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, 110189 China
| | - Qinghua Zhou
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, 110189 China
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384 China
| | - Zhaoxuan Gong
- Department of Computer science, Shenyang Aerospace University, Shenyang, 110136 Liaoning China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001 Australia
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118
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El Asnaoui K. Design ensemble deep learning model for pneumonia disease classification. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2021; 10:55-68. [PMID: 33643764 PMCID: PMC7896551 DOI: 10.1007/s13735-021-00204-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/06/2021] [Accepted: 01/19/2021] [Indexed: 05/23/2023]
Abstract
With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
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Affiliation(s)
- Khalid El Asnaoui
- National School of Applied Sciences (ENSA), Department of Computer Sciences, Mohammed First University, BP: 669, 60000 Oujda, Morocco
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119
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Elzeki OM, Shams M, Sarhan S, Abd Elfattah M, Hassanien AE. COVID-19: a new deep learning computer-aided model for classification. PeerJ Comput Sci 2021; 7:e358. [PMID: 33817008 PMCID: PMC7959596 DOI: 10.7717/peerj-cs.358] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 12/19/2020] [Indexed: 05/09/2023]
Abstract
Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.
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Affiliation(s)
- Omar M. Elzeki
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Mahmoud Shams
- Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt
| | - Shahenda Sarhan
- Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | | | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Egypt, Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
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120
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Li C, Yang Y, Liang H, Wu B. Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl Based Syst 2021; 218:106849. [PMID: 33584016 PMCID: PMC7866884 DOI: 10.1016/j.knosys.2021.106849] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 01/08/2023]
Abstract
The coronavirus disease, called COVID-19, which is spreading fast worldwide since the end of 2019, and has become a global challenging pandemic. Until 27th May 2020, it caused more than 5.6 million individuals infected throughout the world and resulted in greater than 348,145 deaths. CT images-based classification technique has been tried to use the identification of COVID-19 with CT imaging by hospitals, which aims to minimize the possibility of virus transmission and alleviate the burden of clinicians and radiologists. Early diagnosis of COVID-19, which not only prevents the disease from spreading further but allows more reasonable allocation of limited medical resources. Therefore, CT images play an essential role in identifying cases of COVID-19 that are in great need of intensive clinical care. Unfortunately, the current public health emergency, which has caused great difficulties in collecting a large set of precise data for training neural networks. To tackle this challenge, our first thought is transfer learning, which is a technique that aims to transfer the knowledge from one or more source tasks to a target task when the latter has fewer training data. Since the training data is relatively limited, so a transfer learning-based DensNet-121 approach for the identification of COVID-19 is established. The proposed method is inspired by the precious work of predecessors such as CheXNet for identifying common Pneumonia, which was trained using the large Chest X-ray14 dataset, and the dataset contains 112,120 frontal chest X-rays of 14 different chest diseases (including Pneumonia) that are individually labeled and achieved good performance. Therefore, CheXNet as the pre-trained network was used for the target task (COVID-19 classification) by fine-tuning the network weights on the small-sized dataset in the target task. Finally, we evaluated our proposed method on the COVID-19-CT dataset. Experimentally, our method achieves state-of-the-art performance for the accuracy (ACC) and F1-score. The quantitative indicators show that the proposed method only uses a GPU can reach the best performance, up to 0.87 and 0.86, respectively, compared with some widely used and recent deep learning methods, which are helpful for COVID-19 diagnosis and patient triage. The codes used in this manuscript are publicly available on GitHub at (https://github.com/lichun0503/CT-Classification).
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Affiliation(s)
- Chun Li
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Yunyun Yang
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Hui Liang
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Boying Wu
- Department of Mathematics, Harbin Institute of Technology, Harbin, 150006, China
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121
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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. HEALTH AND TECHNOLOGY 2021; 11:411-424. [PMID: 33585153 PMCID: PMC7864619 DOI: 10.1007/s12553-021-00520-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022]
Abstract
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.
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122
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Sarhan A. Run length encoding based wavelet features for COVID-19 detection in X-rays. BJR Open 2021; 3:20200028. [PMID: 33718765 PMCID: PMC7931407 DOI: 10.1259/bjro.20200028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES Introduced in his paper is a novel approach for the recognition of COVID-19 cases in chest X-rays. METHODS The discrete Wavelet transform (DWT) is employed in the proposed system to obtain highly discriminative features from the input chest X-ray image. The selected features are then classified by a support vector machine (SVM) classifier as either normal or COVID-19 cases. The DWT is well-known for its energy compression power. The proposed system uses the DWT to decompose the chest X-ray image into a group of approximation coefficients that contain a small number of high-energy (high-magnitude) coefficients. The proposed system introduces a novel coefficient selection scheme that employs hard thresholding combined with run-length encoding to extract only high-magnitude Wavelet approximation coefficients. These coefficients are utilized as features symbolizing the chest X-ray input image. After applying zero-padding to unify their lengths, the feature vectors are introduced to a SVM which classifies them as either normal or COVID-19 cases. RESULTS The proposed system yields promising results in terms of classification accuracy, which justifies further work in this direction. CONCLUSION The DWT can produce a few features that are highly discriminative. By reducing the dimensionality of the feature space, the proposed system is able to reduce the number of required training images and diminish the space and time complexities of the system. ADVANCES IN KNOWLEDGE Exploiting and reshaping the approximation coefficients can produce discriminative features representing the input image.
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Affiliation(s)
- Ahmad Sarhan
- Department of Computer Engineering, Amman Arab University, Amman, Jordan
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Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, Polidori A, Gandola D, Messa C, Sardanelli F. Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. Eur Radiol Exp 2021; 5:7. [PMID: 33527198 PMCID: PMC7850902 DOI: 10.1186/s41747-020-00203-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 12/17/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. METHODS We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. RESULTS At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. CONCLUSIONS This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
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Affiliation(s)
- Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126, Milan, Italy
- Institute of Biomedical Imaging and Physiology, National Research Council, 20090, Segrate, Milan, Italy
| | - Davide Ippolito
- Department of Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy
| | - Matteo Interlenghi
- Institute of Biomedical Imaging and Physiology, National Research Council, 20090, Segrate, Milan, Italy
| | - Caterina Beatrice Monti
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy
| | - Christian Salvatore
- Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100, Pavia, Italy.
- DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy.
| | - Simone Schiaffino
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
| | - Annalisa Polidori
- DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy
| | - Davide Gandola
- Department of Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy
| | - Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy
- Fondazione Tecnomed, Università degli Studi di Milano-Bicocca, Palazzina Ciclotrone, Via Pergolesi 33, 20900, Monza, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
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Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, Polidori A, Gandola D, Messa C, Sardanelli F. Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. Eur Radiol Exp 2021. [PMID: 33527198 DOI: 10.1101/2020.04.08.20040907] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. METHODS We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. RESULTS At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2. CONCLUSIONS This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
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Affiliation(s)
- Isabella Castiglioni
- Department of Physics, Università degli Studi di Milano-Bicocca, Piazza della Scienza 3, 20126, Milan, Italy
- Institute of Biomedical Imaging and Physiology, National Research Council, 20090, Segrate, Milan, Italy
| | - Davide Ippolito
- Department of Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy
| | - Matteo Interlenghi
- Institute of Biomedical Imaging and Physiology, National Research Council, 20090, Segrate, Milan, Italy
| | - Caterina Beatrice Monti
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy
| | - Christian Salvatore
- Scuola Universitaria Superiore IUSS Pavia, Piazza della Vittoria 15, 27100, Pavia, Italy.
- DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy.
| | - Simone Schiaffino
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
| | - Annalisa Polidori
- DeepTrace Technologies S.R.L., Via Conservatorio 17, 20122, Milan, Italy
| | - Davide Gandola
- Department of Radiology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy
| | - Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126, Milan, Italy
- Fondazione Tecnomed, Università degli Studi di Milano-Bicocca, Palazzina Ciclotrone, Via Pergolesi 33, 20900, Monza, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy
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Karthik R, Menaka R, M H. Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Appl Soft Comput 2021; 99:106744. [PMID: 32989379 PMCID: PMC7510455 DOI: 10.1016/j.asoc.2020.106744] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/27/2020] [Accepted: 09/18/2020] [Indexed: 12/29/2022]
Abstract
COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
| | - R Menaka
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
| | - Hariharan M
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
- School of Computing Sciences Engineering, Vellore Institute of Technology, Chennai, India
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Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031117. [PMID: 33513984 PMCID: PMC7908539 DOI: 10.3390/ijerph18031117] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/16/2021] [Accepted: 01/17/2021] [Indexed: 12/13/2022]
Abstract
With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.
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Affiliation(s)
- Tarik Alafif
- Computer Science Department, Jamoum University College, Umm Al-Qura University, Jamoum 25375, Saudi Arabia
- Correspondence:
| | - Abdul Muneeim Tehame
- Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan;
| | - Saleh Bajaba
- Business Administration Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Ahmed Barnawi
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Saad Zia
- IT Department, Jeddah Cable Company, Jeddah 31248, Saudi Arabia;
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A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images. Biocybern Biomed Eng 2021; 41:239-254. [PMID: 33518878 PMCID: PMC7837255 DOI: 10.1016/j.bbe.2021.01.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 01/06/2021] [Accepted: 01/06/2021] [Indexed: 02/07/2023]
Abstract
The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.
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128
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Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021; 9:e23811. [PMID: 33326405 PMCID: PMC7806275 DOI: 10.2196/23811] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/27/2020] [Accepted: 11/15/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. METHODS A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. RESULTS The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. CONCLUSIONS In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
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Affiliation(s)
- Hafsa Bareen Syeda
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Kevin Wayne Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Farhanuddin Syed
- College of Medicine, Shadan Institute of Medical Sciences, Hyderabad, India
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Feliciano Yu
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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Murugan R, Goel T. E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:8887-8898. [PMID: 33425051 PMCID: PMC7778490 DOI: 10.1007/s12652-020-02688-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 11/08/2020] [Indexed: 06/12/2023]
Abstract
The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.
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Affiliation(s)
- R. Murugan
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam, 788010 India
| | - Tripti Goel
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam, 788010 India
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130
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Ahsan MM, Ahad MT, Soma FA, Paul S, Chowdhury A, Luna SA, Yazdan MMS, Rahman A, Siddique Z, Huebner P. Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:35501-35513. [PMID: 34976572 PMCID: PMC8675556 DOI: 10.1109/access.2021.3061621] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 02/16/2021] [Indexed: 05/19/2023]
Abstract
Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.
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Affiliation(s)
- Md Manjurul Ahsan
- School of Industrial and Systems EngineeringThe University of Oklahoma Norman OK 73019 USA
| | - Md Tanvir Ahad
- School of Aerospace and Mechanical EngineeringThe University of Oklahoma Norman OK 73019 USA
| | - Farzana Akter Soma
- Holy Family Red Crescent Medical College & Hospital Dhaka 1000 Bangladesh
| | - Shuva Paul
- School of Electrical and Computer EngineeringGeorgia Institute of Technology Atlanta GA 30332 USA
| | - Ananna Chowdhury
- Z. H. Sikder Women's Medical College & Hospital Dhaka 1212 Bangladesh
| | | | | | - Akhlaqur Rahman
- School of Industrial Automation and Electrical EngineeringEngineering Institute of Technology Melbourne VIC 3000 Australia
| | - Zahed Siddique
- School of Aerospace and Mechanical EngineeringThe University of Oklahoma Norman OK 73019 USA
| | - Pedro Huebner
- School of Industrial and Systems EngineeringThe University of Oklahoma Norman OK 73019 USA
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131
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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Abstract
This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures. Amid the standard methods for COVID-19 worldwide epidemic prediction, easy statistical, as well as epidemiological methods have got more consideration by researchers and authorities. One main difficulty in controlling the spreading of COVID-19 is the inadequacy and lack of medical tests for detecting as well as identifying a solution. To solve this problem, a few statistical-based advances are being enhanced and turn into a partial resolution up-to some level. To deal with the challenges of the medical field, a broad range of intelligent based methods, frameworks, and equipment have been recommended by Machine Learning (ML) and Deep Learning. As ML and DL have the ability of identifying and predicting patterns in complex large datasets, they are recognized as a suitable procedure for producing effective solutions for the diagnosis of COVID-19. In this paper, a perspective research has been conducted in the applicability of intelligent systems such as ML, DL and others in solving COVID-19 related outbreak issues. The main intention behind this study is (i) to understand the importance of intelligent approaches such as ML and DL for COVID-19 pandemic, (ii) discussing the efficiency and impact of these methods in the prognosis of COVID-19, (iii) the growth in the development of type of ML and advanced ML methods for COVID-19 prognosis,(iv) analyzing the impact of data types and the nature of data along with challenges in processing the data for COVID-19,(v) to focus on some future challenges in COVID-19 prognosis to inspire the researchers for innovating and enhancing their knowledge and research on other impacted sectors due to COVID-19.
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133
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Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning. STUDIES IN BIG DATA 2021. [DOI: 10.1007/978-3-030-74575-2_14] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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134
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A survey of machine learning techniques for detecting and diagnosing COVID-19 from imaging. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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135
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Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal Appl 2021. [PMID: 33994847 DOI: 10.1007/s10044-021-00984-y:1-14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.
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Affiliation(s)
- Ali Narin
- Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
| | - Ceren Kaya
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
| | - Ziynet Pamuk
- Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey
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136
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Sadeghi MH, Omidi H, Sina S. A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images. AVICENNA JOURNAL OF MEDICAL BIOCHEMISTRY 2020. [DOI: 10.34172/ajmb.2020.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed. Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies. Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively. Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.
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Affiliation(s)
- Mohammad Hosein Sadeghi
- Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran
| | - Hamid Omidi
- Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran
| | - Sedigheh Sina
- Department of Nuclear Engineering, School of Mechanical Engineering, Shiraz University, Shiraz, Iran
- Radiation Research Center, School of Mechanical Engineering, Shiraz University, Iran
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137
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Yousri D, Abd Elaziz M, Abualigah L, Oliva D, Al-Qaness MAA, Ewees AA. COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Appl Soft Comput 2020; 101:107052. [PMID: 33519325 PMCID: PMC7837203 DOI: 10.1016/j.asoc.2020.107052] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/21/2020] [Accepted: 12/17/2020] [Indexed: 02/07/2023]
Abstract
Classification of COVID-19 X-ray images to determine the patient’s health condition is a critical issue these days since X-ray images provide more information about the patient’s lung status. To determine the COVID-19 case from other normal and abnormal cases, this work proposes an alternative method that extracted the informative features from X-ray images, leveraging on a new feature selection method to determine the relevant features. As such, an enhanced cuckoo search optimization algorithm (CS) is proposed using fractional-order calculus (FO) and four different heavy-tailed distributions in place of the Lévy flight to strengthen the algorithm performance during dealing with COVID-19 multi-class classification optimization task. The classification process includes three classes, called normal patients, COVID-19 infected patients, and pneumonia patients. The distributions used are Mittag-Leffler distribution, Cauchy distribution, Pareto distribution, and Weibull distribution. The proposed FO-CS variants have been validated with eighteen UCI data-sets as the first series of experiments. For the second series of experiments, two data-sets for COVID-19 X-ray images are considered. The proposed approach results have been compared with well-regarded optimization algorithms. The outcomes assess the superiority of the proposed approach for providing accurate results for UCI and COVID-19 data-sets with remarkable improvements in the convergence curves, especially with applying Weibull distribution instead of Lévy flight.
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Affiliation(s)
- Dalia Yousri
- Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.,Academy of Scientific Research and Technology (ASRT), Egypt
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
| | - Diego Oliva
- Departamento de ComputaciÃsn, Universidad de Guadalajara, CUCEI, Av. RevoluciÃsn 1500, Guadalajara, Jal, Mexico
| | - Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
| | - Ahmed A Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt
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138
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Arias-Londoño JD, Gómez-García JA, Moro-Velázquez L, Godino-Llorente JI. Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:226811-226827. [PMID: 34786299 PMCID: PMC8545248 DOI: 10.1109/access.2020.3044858] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 05/17/2023]
Abstract
Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images to differentiate between controls, pneumonia, or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79, 500 X-Ray images compiled from different sources, including more than 8, 500 COVID-19 examples. Three different experiments following three preprocessing schemes are carried out to evaluate and compare the developed models. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis of different variability issues that might compromise the system and its effects is performed. With the employed methodology, a 91.5% classification accuracy is obtained, with an 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lung region.
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Affiliation(s)
| | - Jorge A. Gómez-García
- Bioengineering and Optoelectronics Laboratory (ByO)Universidad Politécnica de Madrid28031MadridSpain
| | - Laureano Moro-Velázquez
- Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
| | - Juan I. Godino-Llorente
- Bioengineering and Optoelectronics Laboratory (ByO)Universidad Politécnica de Madrid28031MadridSpain
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139
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Chen Y, Jiang G, Li Y, Tang Y, Xu Y, Ding S, Xin Y, Lu Y. A Survey on Artificial Intelligence in Chest Imaging of COVID-19. BIO INTEGRATION 2020. [DOI: 10.15212/bioi-2020-0015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Abstract The coronavirus disease 2019 (COVID-19) has infected more than 9.3 million people and has caused over 0.47 million deaths worldwide as of June 24, 2020. Chest imaging techniques including computed tomography and X-ray scans are indispensable tools in COVID-19 diagnosis
and its management. The strong infectiousness of this disease brings a huge burden for radiologists. In order to overcome the difficulty and improve accuracy of the diagnosis, artificial intelligence (AI)-based imaging analysis methods are explored. This survey focuses on the development of
chest imaging analysis methods based on AI for COVID-19 in the past few months. Specially, we first recall imaging analysis methods of two typical viral pneumonias, which can provide a reference for studying the disease on chest images. We further describe the development of AI-assisted diagnosis
and assessment for the disease, and find that AI techniques have great advantage in this application.
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Affiliation(s)
- Yun Chen
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan, China
| | - Gongfa Jiang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yue Li
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yutao Tang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yanfang Xu
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Siqi Ding
- School of Mathematics, Sun Yat-sen University, Guangzhou, China
| | - Yanqi Xin
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yao Lu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
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140
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Rasheed J, Jamil A, Hameed AA, Aftab U, Aftab J, Shah SA, Draheim D. A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. CHAOS, SOLITONS, AND FRACTALS 2020; 141:110337. [PMID: 33071481 PMCID: PMC7547637 DOI: 10.1016/j.chaos.2020.110337] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/04/2023]
Abstract
While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul 34303, Turkey
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul 34303, Turkey
| | - Usman Aftab
- Department of Pharmacology, University of Health Sciences, Lahore 54700, Pakistan
| | - Javaria Aftab
- Department of Chemistry, Istanbul Technical University, Istanbul 34467, Turkey
| | - Syed Attique Shah
- Department of IT, Balochistan University of Information Technology, Engineering and Management Sciences, Quetta 87300, Pakistan
| | - Dirk Draheim
- Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia
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141
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Tseng VS, Jia-Ching Ying J, Wong ST, Cook DJ, Liu J. Computational Intelligence Techniques for Combating COVID-19: A Survey. IEEE COMPUT INTELL M 2020. [DOI: 10.1109/mci.2020.3019873] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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142
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De Moura J, García LR, Vidal PFL, Cruz M, López LA, Lopez EC, Novo J, Ortega M. Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:195594-195607. [PMID: 34786295 PMCID: PMC8545263 DOI: 10.1109/access.2020.3033762] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/21/2020] [Indexed: 05/18/2023]
Abstract
The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.
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Affiliation(s)
- Joaquim De Moura
- Centro de Investigación CITICUniversidade da Coruña15071 ACoruñaSpain
- Grupo VARPAInstituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña15006 ACoruñaSpain
| | - Lucía Ramos García
- Centro de Investigación CITICUniversidade da Coruña15071 ACoruñaSpain
- Grupo VARPAInstituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña15006 ACoruñaSpain
| | - Plácido Francisco Lizancos Vidal
- Centro de Investigación CITICUniversidade da Coruña15071 ACoruñaSpain
- Grupo VARPAInstituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña15006 ACoruñaSpain
| | - Milena Cruz
- Servicio de RadiodiagnósticoComplexo Hospitalario Universitario de A Coruña (CHUAC)15006 ACoruñaSpain
| | - Laura Abelairas López
- Servicio de RadiodiagnósticoComplexo Hospitalario Universitario de A Coruña (CHUAC)15006 ACoruñaSpain
| | - Eva Castro Lopez
- Servicio de RadiodiagnósticoComplexo Hospitalario Universitario de A Coruña (CHUAC)15006 ACoruñaSpain
| | - Jorge Novo
- Centro de Investigación CITICUniversidade da Coruña15071 ACoruñaSpain
- Grupo VARPAInstituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña15006 ACoruñaSpain
| | - Marcos Ortega
- Centro de Investigación CITICUniversidade da Coruña15071 ACoruñaSpain
- Grupo VARPAInstituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña15006 ACoruñaSpain
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143
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Sharma A, Rani S, Gupta D. Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases. Int J Biomed Imaging 2020; 2020:8889023. [PMID: 33061946 PMCID: PMC7539085 DOI: 10.1155/2020/8889023] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/16/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022] Open
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.
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Affiliation(s)
- Arun Sharma
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Sheeba Rani
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi 110067, India
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144
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Chauhan N, Soni S, Gupta A, Jain U. New and developing diagnostic platforms for COVID-19: A systematic review. Expert Rev Mol Diagn 2020; 20:971-983. [PMID: 32896179 DOI: 10.1080/14737159.2020.1816466] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The starting months of 2020 witnessed a global pandemic of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection. The first case of Coronavirus Disease 2019 (COVID-19) was reported in December, 2019 in Wuhan, China and millions of cases and thousands of deaths were reported within five months. Currently, reverse transcription-polymerase chain reaction (RT-PCR) and computed tomography (CT) scanning are clinically prescribed for COVID-19 detection across the globe. AREAS COVERED This systematic review is focused on currently used diagnostic methods for COVID-19 detection and their future prospects. Online searches on Google Scholar, PubMed and online resources were conducted on the period of year 2017 to mid-2020. Studies investigating laboratory examinations, radiographical analysis, and potential sensors for COVID-19 detection were included. Along with this, the current status of commercially available kits for SARS-CoV-2 coronavirus detection is discussed. EXPERT OPINION The search has identified the potential applications of nucleic acid technology, diagnostics radiology examinations, and in-vitro diagnostic kits in detection of COVID-19 infections. Despite having their own limitations of each technology, the emerging diagnostic technologies for COVID-19 detection along with undergoing clinical trials are summarized suggesting more collaborations and funding are required for fast track clinical trials.
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Affiliation(s)
- Nidhi Chauhan
- Amity Institute of Nanotechnology (AINT), Amity University Uttar Pradesh (AUUP) , Noida, 201313, INDIA
| | - Shringika Soni
- Amity Institute of Nanotechnology (AINT), Amity University Uttar Pradesh (AUUP) , Noida, 201313, INDIA
| | - Abhinandan Gupta
- Amity Institute of Nanotechnology (AINT), Amity University Uttar Pradesh (AUUP) , Noida, 201313, INDIA
| | - Utkarsh Jain
- Amity Institute of Nanotechnology (AINT), Amity University Uttar Pradesh (AUUP) , Noida, 201313, INDIA
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145
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Wang Z, Liu Q, Dou Q. Contrastive Cross-Site Learning With Redesigned Net for COVID-19 CT Classification. IEEE J Biomed Health Inform 2020; 24:2806-2813. [PMID: 32915751 PMCID: PMC8545175 DOI: 10.1109/jbhi.2020.3023246] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 08/13/2020] [Accepted: 09/02/2020] [Indexed: 11/09/2022]
Abstract
The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.
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Affiliation(s)
- Zhao Wang
- College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina
| | - Quande Liu
- Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
| | - Qi Dou
- Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
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146
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Jain G, Mittal D, Thakur D, Mittal MK. A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybern Biomed Eng 2020; 40:1391-1405. [PMID: 32921862 PMCID: PMC7476608 DOI: 10.1016/j.bbe.2020.08.008] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/26/2020] [Accepted: 08/30/2020] [Indexed: 01/20/2023]
Abstract
Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.
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Affiliation(s)
- Govardhan Jain
- Department of Electrical Engineering, Medical Engineering and Computer Science (EMI), Hochschule Offenburg, Offenburg, Germany
| | - Deepti Mittal
- Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
| | - Daksh Thakur
- Department of Electrical Engineering, Medical Engineering and Computer Science (EMI), Hochschule Offenburg, Offenburg, Germany
| | - Madhup K Mittal
- Department of Mechanical Engineering, Thapar Institute of Engineering & Technology, Patiala, Punjab, India
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147
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Abstract
In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.
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Affiliation(s)
- Junaid Shuja
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Islamabad, Pakistan
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Eisa Alanazi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Waleed Alasmary
- Department of Computer Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
- Center of Innovation and Development in Artificial Intelligence, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Abdulaziz Alashaikh
- Computer Engineering and Networks Department, University of Jeddah, Jeddah, Saudi Arabia
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148
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Gomes JC, Barbosa VADF, Santana MA, Bandeira J, Valença MJS, de Souza RE, Ismael AM, dos Santos WP. IKONOS: an intelligent tool to support diagnosis of COVID-19 by texture analysis of X-ray images. RESEARCH ON BIOMEDICAL ENGINEERING 2020. [PMCID: PMC7471577 DOI: 10.1007/s42600-020-00091-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Purpose In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.
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Affiliation(s)
- Juliana C. Gomes
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | | | - Maíra A. Santana
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | - Jonathan Bandeira
- Polytechnique School of the University of Pernambuco, Recife, Brazil
| | | | | | - Aras Masood Ismael
- Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
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149
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Swapnarekha H, Behera HS, Nayak J, Naik B. Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. CHAOS, SOLITONS, AND FRACTALS 2020; 138:109947. [PMID: 32836916 PMCID: PMC7256553 DOI: 10.1016/j.chaos.2020.109947] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 05/09/2023]
Abstract
The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.
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Affiliation(s)
- H Swapnarekha
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Himansu Sekhar Behera
- Department of Information Technology, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
| | - Janmenjoy Nayak
- Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India
| | - Bighnaraj Naik
- Department of Computer Application, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur-768018, Odisha, India
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150
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Oh Y, Park S, Ye JC. Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2688-2700. [PMID: 32396075 DOI: 10.1109/tmi.2020.2993291] [Citation(s) in RCA: 349] [Impact Index Per Article: 87.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
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