SYSTEMATIC REVIEW article
Sec. Cardiovascular Imaging
Volume 8 - 2021 | https://doi.org/10.3389/fcvm.2021.638011
Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review
- 1Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- 2Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- 3Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran
- 4Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- 5Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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.
First identified in Wuhan, China, severe pneumonia caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quickly spread all over the world. The resultant disorder was named coronavirus disease (COVID-19) (1, 2). COVID-19 has various clinical symptoms, including fever, cough, dyspnea, fatigue, myalgia, headache, and gastrointestinal complications (3–5). Diagnosis of COVID-19 infection through RT-PCR on nasopharyngeal and throat swab samples has been reported to yield positive results in 30–70% of cases (6, 7). On the other hand, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively (7–9). The most typical radiological signs in these patients include multifocal and bilateral ground-glass opacities and consolidations, particularly in the peripheral and basal sites (10). However, interpretation of the results of these imaging techniques by expert radiologists might encounter some problems leading to reduced sensitivity (11). Artificial intelligence has recently gained the attention of both clinicians and researchers for the appropriate management of the COVID-19 pandemic (12). As an accurate method, artificial intelligence is able to identify abnormal patterns of CT and X-ray images. Using this method, it is possible to assess certain segment regions and take precise structures in chest CT images facilitating diagnostic purposes. Artificial intelligence methods have been shown to detect COVID-19 and distinguish this condition from other pulmonary disorders and community-acquired pneumonia (13). Both deep learning and machine learning approaches have been used to predict different aspects of the COVID-19 outbreak. Support vector and random forest are among the most applied machine learning methods, while Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Residual Neural network are among the deep learning methods used in this regard (14). In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for the purpose of COVID-19 diagnosis and compared their performance.
The research question was: “What are the applications of machine learning techniques and their performances in COVID-19 diagnosis using X-ray images?”. The search of the present review was based on the PICO elements, which were as follows:
• P (Problem/Patient/Population): Patients' CT scans and Chest X-rays.
• I (Intervention/Indicator): Machine/deep learning models for diagnosis of Covid-19 patients
• C (Comparison): Ground truth or reference standards
• O (Outcome): Performance measurements including accuracy, AUC score, sensitivity, and specificity.
In other words, we were looking for publications that evaluated the performance of any machine learning or deep learning approaches based on inclusion and exclusion criteria. Studies that used other types of medical image modalities (e.g., ultrasound images) were excluded. An electronic search was conducted on PubMed, Google Scholar, Scopus, Embase, arXiv, and medRxiv for finding the relevant literature. Duplicate studies were removed. Studies that were cited within the retrieved papers were reviewed for finding missing studies. For identifying proper journal papers and conference proceedings, investigators screened the title and abstracts based on inclusion and exclusion criteria independently. Finally, considering the inclusion and exclusion criteria, investigators identified the eligible publications in this stage independently.
The following inclusion criteria were used in the selection of the articles: (1) Studies that applied machine learning or deep learning algorithms, (2) Studies that evaluated the measurement of model outcomes in comparison with ground truth or gold standards, and (3) Studies that used algorithms to analyze radiographic images (CT scan, Chest X-ray, etc.).
The following studies were excluded: (1) Studies that used any machine learning or deep learning approaches for problems not directly related to the COVID-19 imaging, (2) Studies that used other artificial intelligence techniques or classic computer vision approaches, (3) Studies that did not provide a clear explanation of the machine learning or deep learning model that was used to solve their problem, and (4) Review studies. The latter were excluded as we did not aim to review the data on an original level without any second-hand interpretations (summation, inferences, etc.).
Figure 1 shows the flowchart of the study design.
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) chart showing the process of systematic identification, screening, and selection of articles.
We obtained 105 studies that used machine or deep learning methods to assess chest images of COVID-19 patients. These studies have used different analytical methods. For instance, Ardakani et al. (15) have assessed radiological features of CT images obtained from patients with COVID-19 and non-COVID-19 pneumonia. They used decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble classifiers to find the computer-aided diagnosis system with the best performance in distinguishing COVID-19 patients from non-COVID-19 pneumonia. They reported that site and distribution of pulmonary involvement, the quantity of the pulmonary lesions, ground-glass opacity, and crazy-paving as the most important characteristics for differentiation of these two sets of patients. Their computer-aided diagnosis method yielded the accuracy of 91.94%, using an ensemble (COVIDiag) classifier. Alazab et al. (16) have developed an artificial-intelligence method based on a deep CNN to evaluate chest X-ray images and detection of COVID-19 patients. Their method yielded an F-measure ranging from 95 to 99%. Notably, three predicting strategies could forecast the numbers of COVID-19 confirmations, recoveries, and mortalities over the upcoming week. The average accuracy of the prediction models were 94.80 and 88.43% in two different countries. Albahli has applied deep learning-based models on CT images of COVID-19 patients. He has demonstrated a high performance of a Deep Neural Network model in detecting COVID-19 patients and has offered an efficient assessment of chest-related disorders according to age and sex. His proposed model has yielded 89% accuracy in terms of GAN-based synthetic data (17). Automatic detection of COVID-19 based on X-ray images has been executed through the application of three deep learning models, including Inception ResNetV2, InceptionNetV3, and NASNetLarge. The best results have been obtained from InceptionNetV3, which yielded the accuracy levels of 98.63 and 99.02% with and without application of data augmentation in model training, respectively (18). Alsharman et al. (19) have used the CNN method to detect COVID-19 based on chest CT images in the early stages of disease course. Authors have reported high accuracy of GoogleNet CNN architecture for diagnosis of COVID-19. Altan et al. (20) have used a hybrid model comprising two-dimensional curvelet transformation, chaotic salp swarm algorithm, and deep learning methods for distinguishing COVID-19 from other pneumonia cases. Application of their proposed model on chest X-ray images has led to accurate diagnosis of COVID-19 patients (Accuracy = 99.69%, Sensitivity = 99.44% and Specificity = 99.81%). Apostolopoulos et al. (21) have used a certain CNN strategy, namely MobileNet on X-Ray images of COVID-19 patients. This method has yielded more than 99% accuracy in the diagnosis of COVID-19. In another study, Ardakani et al. (22) used 10 CNN strategies, namely AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception, to differentiate COVID-19 cases from non-COVID-19 patients. They have demonstrated the best diagnostic values for ResNet-101 and Xception, both of them having area under curve (AUC) values higher than 0.99 which is superior to the performance of the radiologist. Das et al. (23) have used the CNN model Truncated InceptionNet to diagnose COVID-19 from other non-COVID and/or healthy cases based on chest X-ray. Their suggested model yielded AUC of 1.0 in distinguishing COVID-19 patients from combined Pneumonia and healthy subjects. Tables 1, 2 summarize the features of studies which adopted machine learning methods in CT images and chest X-ray of COVID-19 patients, respectively.
Machine and deep learning methods have been proven as valuable strategies to assess massive high-dimensional characteristics of medical images. CT or X-Ray findings of COVID-19 patients have similarities with other atypical and viral pneumonia diseases. Therefore, machine and deep learning methods might facilitate automatic discrimination of COVID-19 from other pneumonia conditions. The differential diagnosis of COVID also includes drug-induced diseases or immune pneumonitis. However, most of the studies reviewed here lack these kinds of samples. This point is the limitation of these studies. Different methods, such as Ensemble, VGG-16, ResNet, InceptionNetV3, MobileNet v2, Xception, CNN, VGG16, Truncated Inception Net, and KNN, have been used for the purpose of assessment of chest images of COVID-19 patients. Notably, the application of these methods on X-rays has offered promising results. Such a finding is particularly important since X-rays are easily accessible and low cost. These methods not only can diagnose COVID-19 patients from non-COVID pneumonia cases, but can also predict the severity of COVID-19 pneumonia and the risk of short-term mortality. In spite of the low expense of X-ray compared with CT images, the numbers of studies that assessed these two types of imaging using machine/deep learning methods are not meaningfully different. However, few studies have used these methods on both types of imaging (25, 29, 40). CNN-based methods have achieved accuracy values above 99% in classifying COVID-19 patients from other cases of pneumonia or related disorders, as reported by several independent studies, suggesting these strategies as screening methods for initial evaluation of COVID-19 cases.
Although both deep learning and machine learning strategies can be used for the mentioned purpose, they differ in some respects. For instance, deep learning methods usually need a large amount of labeled training data to make a concise conclusion. However, machine learning can apply a small amount of data delivered by users. Moreover, deep learning methods need high-performance hardware. Machine learning, on the other hand, needs features to be precisely branded by users, deep learning generates novel features by itself, thus requires more time to train. Machine learning classifies tasks into small fragments and subsequently combines obtained results into one conclusion, whereas deep learning resolves the problems using end-to-end principles.
Several studies have diagnosed COVID-19 patients through the application of machine learning methods rather than using deep learning methods by retrieving the features from the images. These studies have yielded high recognition outcomes and have the advantage of high learning speed (12). Pre-processing is an essential step for reducing the impacts of intensity variations in CT slices and getting rid of noise. Subsequent thresholding and morphological operations have also enhanced the analytical performance. Data augmentation and histogram equalization are among the most applied preprocessing methods.
One of the most promising approaches used in the included studies was transfer learning. Transfer learning is defined as using model knowledge on a huge dataset (which is referred to as the “pre-trained model”) and transferring it to use on a new problem. This is very useful in settings like medical imaging, where there is a limited number of labeled data (113). Previous studies showed favorable outcomes of the transfer learning approaches in medical imaging tasks (114, 115). Among the included studies, Bridge et al. (25) even reached 100% classification accuracy on COVID-19 using the pre-trained InceptionV3.
The availability of public databases of CT and X-ray images of patients with COVID-19 has facilitated the application of machine learning methods on large quantities of clinical images and execution of training and verification steps. However, since these images have come from various institutes using different scanners, preprocessing of the obtained data is necessary to make them uniform and facilitate further analysis (12). Appraisal of demographic and clinical data of COVID-19 patients and their association with CT/ X-ray images features as well as the accuracy of machine learning prediction methods would provide more valuable information in the stratification of COVID-19 patients. Moreover, one of the major challenges of deep learning models in medical applications is its unexplainable features due to its black-box nature, which should be solved (116). Future studies can focus on approaches that provide interpretation besides black-box predictions.
Deep and machine learning methods have high accuracy in the differentiation of COVID-19 from non-COVID-19 pneumonia based on chest images. These techniques have facilitated the automatic evaluation of these images. However, deep learning methods suffer from the absence of transparency and interpretability, as it is not possible to identify the exact imaging feature that has been applied to define the output (13). As no single strategy has the capacity to distinguish all pulmonary disorders based merely on the imaging presentation on chest CT scans, the application of multidisciplinary approaches is suggested for overcoming diagnostic problems (13).
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
HM-R, MN, and AG-L collected the data and designed the tables. MT and SG-F designed the study, wrote the draft, and revised it. All the authors read the draft and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
1. Wang L, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest X-ray images. arXiv. (2020) Preprint arXiv:200309871. doi: 10.1038/s41598-020-76550-z
2. Ghafouri-Fard S, Noroozi R, Vafaee R, Branicki W, Poṡpiech E, Pyrc K, et al. Effects of host genetic variations on response to, susceptibility and severity of respiratory infections. Biomed Pharmacother. (2020) 128:110296. doi: 10.1016/j.biopha.2020.110296
3. Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, et al. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv. (2020).
4. Samsami M, Mehravaran E, Tabarsi P, Javadi A, Arsang-Jang S, Komaki A, et al. Clinical and demographic characteristics of patients with COVID-19 infection: statistics from a single hospital in Iran. Human Antibodies. (2020) 1–6. doi: 10.3233/HAB-200428
5. Ghafouri-Fard S, Noroozi R, Omrani MD, Branicki W, Pośpiech E, Sayad A, et al. Angiotensin converting enzyme: a review on expression profile and its association with human disorders with special focus on SARS-CoV-2 infection. Vascular Pharmacol. (2020) 130:106680. doi: 10.1016/j.vph.2020.106680
6. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv. (2020) 14:1–9. doi: 10.1101/2020.02.14.20023028
7. Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology. (2020) 296:1–2. doi: 10.1148/radiol.2020200432
8. Zhang J, Tian S, Lou J, Chen Y. Familial cluster of COVID-19 infection from an asymptomatic. Crit Care. (2020) 24:1–3. doi: 10.1186/s13054-020-2817-7
9. Lei Y, Zhang H-W, Yu J, Patlas MN. COVID-19 Infection: Early Lessons. Los Angeles, CA: Sage (2020).
10. Rousan LA, Elobeid E, Karrar M, Khader Y. Chest x-ray findings and temporal lung changes in patients with COVID-19 pneumonia. BMC Pulmonary Med. (2020) 20:1–9. doi: 10.1186/s12890-020-01286-5
11. Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology. (2020) 296:1–8. doi: 10.1148/radiol.2020200823
12. Ozsahin I, Sekeroglu B, Musa MS, Mustapha MT, Uzun Ozsahin D. Review on diagnosis of COVID-19 from chest CT images using artificial intelligence. Comput Math Methods Med. (2020) 2020:1–10. doi: 10.1155/2020/9756518
13. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology. (2020) 296:E65–71. doi: 10.1148/radiol.2020200905
14. rekha Hanumanthu S. Role of intelligent computing in COVID-19 prognosis: a state-of-the-art review. Chaos Solitons Fractals. (2020) 138:109947. doi: 10.1016/j.chaos.2020.109947
15. Abbasian Ardakani A, Acharya UR, Habibollahi S, Mohammadi A. COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings. Eur Radiol. (2020) 31:1–10. doi: 10.1007/s00330-020-07087-y
16. Alazab M, Awajan A, Mesleh A, Abraham A, Jatana V, Alhyari S. COVID-19 prediction and detection using deep learning. Int J Comput Information Syst Indus Manage Appl. (2020) 12:168–81. doi: 10.1016/j.chaos.2020.110338
17. Albahli S. Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. Int J Med Sci. (2020) 17:1439–48. doi: 10.7150/ijms.46684
18. Albahli S, Albattah W. Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms. J Xray Sci Technol. (2020) 28:841–50. doi: 10.3233/XST-200720
19. Alsharman N, Jawarneh I. GoogleNet CNN neural network towards chest CT-coronavirus medical image classification. J Comput Sci. (2020) 16:620–5 doi: 10.3844/jcssp.2020.620.625
20. Altan A, Karasu S. Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique. Chaos Solitons Fractals. (2020) 140:110071. doi: 10.1016/j.chaos.2020.110071
21. Apostolopoulos ID, Aznaouridis SI, Tzani MA. Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng. (2020) 40:1–8. doi: 10.1007/s40846-020-00529-4
22. Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med. (2020) 121:103795. doi: 10.1016/j.compbiomed.2020.103795
23. Das D, Santosh KC, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-rays. Phys Eng Sci Med. (2020) 43:1–11. doi: 10.21203/rs.3.rs-20795/v1
24. Aswathy SU, Jarin T, Mathews R, Nair LM, Rroan M. CAD systems for automatic detection and classification of COVID-19 in nano CT lung image by using machine learning technique. Int J Pharm Res. (2020) 12:1865–70. doi: 10.31838/ijpr/2020.12.02.247
25. Bridge J, Meng Y, Zhao Y, Du Y, Zhao M, Sun R, et al. Introducing the GEV activation function for highly unbalanced data to develop COVID-19 diagnostic models. IEEE J Biomed Health Inform. (2020) 24:1–10. doi: 10.1109/JBHI.2020.3012383
26. Butt C, Gill J, Chun D, Babu BA. Deep learning system to screen coronavirus disease 2019 pneumonia. Appl Intell. (2020) 6:1–7. doi: 10.1007/s10489-020-01714-3
27. Dey N, Rajinikanth V, Fong SJ, Kaiser MS, Mahmud M. Social group optimization-assisted Kapur's entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images. Cognit Comput. (2020) 12:1–13. doi: 10.20944/preprints202005.0052.v1
28. Kermany D, Zhang K, Goldbaum M. Labeled optical coherence tomography (OCT) and Chest X-Ray images for classification. Mendeley Data. (2018) 2. doi: 10.17632/RSCBJBR9SJ.2
29. El Asnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn. (2020) 1–12. doi: 10.1080/07391102.2020.1767212
30. Han Z, Wei B, Hong Y, Li T, Cong J, Zhu X, et al. Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging. (2020) 39:2584–94.doi: 10.1109/TMI.2020.2996256
31. Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun. (2020) 11:4080. doi: 10.1038/s41467-020-17971-2
32. Hasan AM, Al-Jawad MM, Jalab HA, Shaiba H, Ibrahim RW, Al-Shamasneh AR. Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features. Entropy. (2020) 22:517. doi: 10.3390/e22050517
33. Hu S, Gao Y, Niu Z, Jiang Y, Li L, Xiao X, et al. Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access. (2020) 8:118869–83. doi: 10.1109/ACCESS.2020.3005510
34. Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M. Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. J Biomol Struct Dyn. (2020) 1–8. doi: 10.1080/07391102.2020.1788642
35. Kang H, Xia L, Yan F, Wan Z, Shi F, Yuan H, et al. Diagnosis of coronavirus disease 2019 (COVID-19) with structured latent multi-view representation learning. IEEE Trans Med Imaging. (2020) 39:2606–14. doi: 10.1109/TMI.2020.2992546
36. Lessmann N, Sánchez CI, Beenen L, Boulogne LH, Brink M, Calli E, et al. Automated assessment of CO-RADS and chest CT severity scores in patients with suspected COVID-19 using artificial intelligence. Radiology. (2020) 202439.
37. Li Y, Dong W, Chen J, Cao S, Zhou H, Zhu Y, et al. Efficient and effective training of COVID-19 classification networks with self-supervised dual-track learning to rank. IEEE J Biomed Health Inform. (2020) 24:1–10. doi: 10.1109/JBHI.2020.3018181
38. Liu C, Wang X, Liu C, Sun Q, Peng W. Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning. Biomed Eng Online. (2020) 19:66.doi: 10.1186/s12938-020-00809-9
39. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med. (2020) 26:1224–8. doi: 10.1038/s41591-020-0931-3
40. Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals. (2020) 138:109944. doi: 10.1016/j.chaos.2020.109944
41. Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK. Deep transfer learning based classification model for COVID-19 disease. Ing Rech Biomed. (2020) 1–6. doi: 10.1016/j.irbm.2020.05.003
42. Peng Y, Tang YX, Lee S, Zhu Y, Summers RM, Lu Z. COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature. ArXiv. (2020). doi: 10.1109/TBDATA.2020.3035935
43. Pu J, Leader J, Bandos A, Shi J, Du P, Yu J, et al. Any unique image biomarkers associated with COVID-19? Eur Radiol. (2020) 30:1–7. doi: 10.1007/s00330-020-06956-w
44. Raajan NR, Lakshmi VSR, Prabaharan N. Non-invasive technique-based novel corona (COVID-19) virus detection using CNN. Natl Acad Sci Lett. (2020) 1–4. doi: 10.1007/s40009-020-01009-8
45. Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK. Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays. IEEE Access. (2020) 8:115041–50. doi: 10.1109/ACCESS.2020.3003810
46. Sakagianni A, Feretzakis G, Kalles D, Koufopoulou C, Kaldis V. Setting up an easy-to-use machine learning pipeline for medical decision support: a case study for COVID-19 diagnosis based on deep learning with CT scans. Stud Health Technol Inform. (2020) 272:13–6. doi: 10.3233/SHTI200481
47. Sharma S. Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients. Environ Sci Pollut Res Int. (2020) 27:1–9. doi: 10.21203/rs.3.rs-23863/v1
48. Singh D, Kumar V, Vaishali, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis. (2020) 39:1379–89. doi: 10.1007/s10096-020-03901-z
49. Song J, Wang H, Liu Y, Wu W, Dai G, Wu Z, et al. End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT. Eur J Nucl Med Mol Imaging. (2020) 47:1–9. doi: 10.1007/s00259-020-04929-1
50. Wang J, Bao Y, Wen Y, Lu H, Luo H, Xiang Y, et al. Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Trans Med Imaging. (2020) 39:2572–83.doi: 10.1109/TMI.2020.2994908
51. Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J. (2020) 56:2000775.doi: 10.1183/13993003.00775-2020
52. Warman A, Warman P, Sharma A, Parikh P, Warman R, Viswanadhan N, et al. Interpretable artificial intelligence for COVID-19 diagnosis from chest CT reveals specificity of ground-glass opacities. medRxiv. (2020) 1–13. doi: 10.1101/2020.05.16.20103408
53. Wu X, Hui H, Niu M, Li L, Wang L, He B, et al. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur J Radiol. (2020) 128:109041. doi: 10.1016/j.ejrad.2020.109041
54. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering. (2020) 6:1–7. doi: 10.1016/j.eng.2020.04.010
55. Xu Y, Ma L, Yang F, Chen Y, Ma K, Yang J, et al. A collaborative online AI engine for CT-based COVID-19 diagnosis. medRxiv. (2020). doi: 10.1101/2020.05.10.20096073
56. Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y. Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fractals. (2020) 140:110153. doi: 10.1016/j.chaos.2020.110153
57. Yang S, Jiang L, Cao Z, Wang L, Cao J, Feng R, et al. Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study. Ann Transl Med. (2020) 8:450. doi: 10.21037/atm.2020.03.132
58. Yu Z, Li X, Sun H, Wang J, Zhao T, Chen H, et al. Rapid identification of COVID-19 severity in CT scans through classification of deep features. Biomed Eng Online. (2020) 19:63.doi: 10.1186/s12938-020-00807-x
59. Al-Karawi D, Al-Zaidi S, Polus N, Jassim S. Machine learning analysis of chest CT scan images as a complementary digital test of coronavirus (COVID-19) patients. medRxiv. (2020) 1–8. doi: 10.1101/2020.04.13.20063479
60. Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 detection with multi-task deep learning approaches. arXiv. (2020) Preprint arXiv:200403747.
61. Barstugan M, Ozkaya U, Ozturk S. Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv. (2020) Preprint arXiv:200309424.
62. Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep. (2020) 10:1–11. doi: 10.1101/2020.02.25.20021568
63. Farid AA, Selim GI, Awad H, Khater A. A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19). Int J Sci Eng Res. (2020) 11:1–9. doi: 10.14299/ijser.2020.03.02
64. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis. arXiv. (2020) Preprint arXiv:200305037.
65. Jin C, Chen W, Cao Y, Xu Z, Zhang X, Deng L, et al. Development and evaluation of an AI system for COVID-19 diagnosis. medRxiv. (2020) 11:1–14. doi: 10.1101/2020.03.20.20039834
66. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, et al. AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv. (2020). doi: 10.1101/2020.03.19.20039354
67. Kassani SH, Kassasni PH, Wesolowski MJ, Schneider KA, Deters R. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning-based approach. arXiv. (2020) Preprint arXiv:200410641.
68. Ozkaya U, Ozturk S, Barstugan M. Coronavirus (COVID-19) classification using deep features fusion and ranking technique. arXiv. (2020) Preprint arXiv:200403698. doi: 10.1007/978-3-030-55258-9_17
69. Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, et al. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification. arXiv. (2020) Preprint arXiv:200309860. doi: 10.1088/1361-6560/abe838
70. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv. (2020) 1–13. doi: 10.1101/2020.03.12.20027185
71. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. (2020) 43:635–40. doi: 10.1007/s13246-020-00865-4
72. Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed. (2020) 196:105608. doi: 10.1016/j.cmpb.2020.105608
73. Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, et al. Can AI help in screening viral and COVID-19 pneumonia? IEEE Access. (2020) 8:132665–76. doi: 10.1109/ACCESS.2020.3010287
74. Civit-Masot J, Luna-Perejón F, Morales MD, Civit A. Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images. Appl Sci. (2020) 10:4640. doi: 10.3390/app10134640
75. Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT. New machine learning method for image-based diagnosis of COVID-19. PLoS ONE. (2020) 15:e0235187. doi: 10.1371/journal.pone.0235187
76. Hassantabar S, Ahmadi M, Sharifi A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals. (2020) 140:110170. doi: 10.1016/j.chaos.2020.110170
77. Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked. (2020) 20:100412.doi: 10.1016/j.imu.2020.100412
78. Khan AI, Shah JL, Bhat MM. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed. (2020) 196:105581.doi: 10.1016/j.cmpb.2020.105581
79. Khuzani AZ, Heidari M, Shariati SA. COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images. medRxiv. (2020).
80. Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, et al. COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation. J Med Internet Res. (2020) 22:e19569. doi: 10.2196/19569
81. Loey M, Smarandache F, Khalifa NEM. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry. (2020) 12:651. doi: 10.3390/sym12040651
82. Mahmud T, Rahman MA, Fattah SA. CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization. Comput Biol Med. (2020) 122:103869. doi: 10.1016/j.compbiomed.2020.103869
83. Martínez F, Martínez F, Jacinto E. Performance evaluation of the NASnet convolutional network in the automatic identification of COVID-19. Int J Adv Sci Engin Information Technol. (2020) 10:662–7. doi: 10.18517/ijaseit.10.2.11446
84. Minaee S, Kafieh R, Sonka M, Yazdani S, Jamalipour Soufi G. Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal. (2020) 65:101794.doi: 10.1016/j.media.2020.101794
85. Narayan Das N, Kumar N, Kaur M, Kumar V, Singh D. Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays. Ing Rech Biomed. (2020) 1–7.doi: 10.1016/j.irbm.2020.07.001
86. Nour M, Cömert Z, Polat K. A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl Soft Comput. (2020) 97:1–14.doi: 10.1016/j.asoc.2020.106580
87. Novitasari DCR, Hendradi R, Caraka RE, Rachmawati Y, Fanani NZ, Syarifudin A, et al. Detection of COVID-19 chest x-ray using support vector machine and convolutional neural network. Commun Math Biol Neurosci. (2020) 2020:1–19. doi: 10.28919/cmbn/4765
88. Oh Y, Park S, Ye JC. Deep Learning COVID-19 Features on CXR using limited training data sets. IEEE Trans Med Imaging. (2020) 39:2688–700. doi: 10.1109/TMI.2020.2993291
89. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. (2020) 121:103792. doi: 10.1016/j.compbiomed.2020.103792
90. Pandit MK, Banday SA. SARS n-CoV2-19 detection from chest x-ray images using deep neural networks. Int J Pervasive Comput Commun. (2020) 16:1–9. doi: 10.1108/IJPCC-06-2020-0060
91. Pereira RM, Bertolini D, Teixeira LO, Silla CN Jr., Costa YMG. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed. (2020) 194:105532. doi: 10.1016/j.cmpb.2020.105532
92. Rahaman MM, Li C, Yao Y, Kulwa F, Rahman MA, Wang Q, et al. Identification of COVID-19 samples from chest X-Ray images using deep learning: a comparison of transfer learning approaches. J Xray Sci Technol. (2020) 28:1–19. doi: 10.3233/XST-200715
93. Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked. (2020) 19:100360. doi: 10.1016/j.imu.2020.100360
94. Sethy PK, Behera SK, Ratha PK, Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Int J Math Eng Manage Sci. (2020) 5:643–51.doi: 10.33889/IJMEMS.2020.5.4.052
95. Shibly KH, Dey SK, Islam MT, Rahman MM. COVID faster R-CNN: a novel framework to diagnose novel coronavirus disease (COVID-19) in X-ray images. Inform Med Unlocked. (2020) 20:100405. doi: 10.1016/j.imu.2020.100405
96. Togaçar M, Ergen B, Cömert Z. COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest X-ray images using fuzzy color and stacking approaches. Comput Biol Med. (2020) 121:103805. doi: 10.1016/j.compbiomed.2020.103805
97. Toraman S, Alakus TB, Turkoglu I. Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos Solitons Fractals. (2020) 140:110122. doi: 10.1016/j.chaos.2020.110122
98. Tsiknakis N, Trivizakis E, Vassalou EE, Papadakis GZ, Spandidos DA, Tsatsakis A, et al. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays. Exp Ther Med. (2020) 20:727–35. doi: 10.3892/etm.2020.8797
99. Tuncer T, Dogan S, Ozyurt F. An automated residual exemplar local binary pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image. Chemometr Intell Lab Syst. (2020) 203:104054. doi: 10.1016/j.chemolab.2020.104054
100. Ucar F, Korkmaz D. COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses. (2020) 140:109761. doi: 10.1016/j.mehy.2020.109761
101. Vaid S, Kalantar R, Bhandari M. Deep learning COVID-19 detection bias: accuracy through artificial intelligence. Int Orthop. (2020) 44:1539–42. doi: 10.1007/s00264-020-04609-7
102. Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR. CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access. (2020) 8:91916–23. doi: 10.1109/ACCESS.2020.2994762
103. Yildirim M, Cinar A. A deep learning based hybrid approach for covid-19 disease detections. Traitement Signal. (2020) 37:461–8. doi: 10.18280/ts.370313
104. Yoo SH, Geng H, Chiu TL, Yu SK, Cho DC, Heo J, et al. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front Med. (2020) 7:427. doi: 10.3389/fmed.2020.00427
105. Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv. (2020) Preprint arXiv:200310769.
106. Hall LO, Paul R, Goldgof DB, Goldgof GM. Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv. (2020) 40:1–14. doi: 10.36227/techrxiv.12083964
107. Hammoudi K, Benhabiles H, Melkemi M, Dornaika F, Arganda-Carreras I, Collard D, et al. Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the Era of COVID-19. arXiv. (2020) Preprint arXiv:200403399.
108. Hemdan EE-D, Shouman MA, Karar ME. Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv. (2020) Preprint arXiv:200311055.
109. Jain G, Mittal D, Thakur D, Mittal MK. A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybernet Biomed Eng. (2020). doi: 10.1016/j.bbe.2020.08.008
110. Luz E, Silva PL, Silva R, Moreira G. Towards an efficient deep learning model for covid-19 patterns detection in x-ray images. arXiv. (2020) 31:1–10.
111. Ozturk S, Ozkaya U, Barstugan M. Classification of coronavirus images using shrunken features. medRxiv. (2020). doi: 10.1101/2020.04.03.20048868
112. Zhang J, Xie Y, Li Y, Shen C, Xia Y. Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv. (2020) Preprint arXiv:200312338.
113. Ravishankar H, Sudhakar P, Venkataramani R, Thiruvenkadam S, Annangi P, Babu N, et al. Understanding the mechanisms of deep transfer learning for medical images. In: Deep Learning and Data Labeling for Medical Applications: Springer (2016). p. 188–96.
114. Hosny KM, Kassem MA, Foaud MM. Classification of skin lesions using transfer learning and augmentation with Alex-net. PLoS ONE. (2019) 14:e0217293. doi: 10.1371/journal.pone.0217293
115. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett. (2019) 125:1–6. doi: 10.1016/j.patrec.2019.03.022
116. Singh A, Sengupta S, Lakshminarayanan V. Explainable deep learning models in medical image analysis. J Imaging. (2020) 6:52. doi: 10.3390/jimaging6060052
Keywords: COVID-19, machine learning, detection, biomarker, X-ray image
Citation: Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M and Ghafouri-Fard S (2021) Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Front. Cardiovasc. Med. 8:638011. doi: 10.3389/fcvm.2021.638011
Received: 16 January 2021; Accepted: 23 February 2021;
Published: 25 March 2021.
Edited by:Salah D. Qanadli, University of Lausanne, Switzerland
Reviewed by:Beigelman Catherine, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland
Sara Hosseinzadeh Kassani, University of British Columbia, Canada
Copyright © 2021 Mohammad-Rahimi, Nadimi, Ghalyanchi-Langeroudi, Taheri and Ghafouri-Fard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mohammad Taheri, firstname.lastname@example.org; Soudeh Ghafouri-Fard, email@example.com