AI-based COVID-19 disease detection in medical images: Advancements and implications in healthcare
Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v6i3.698
Abstract
Keywords
medical images; CNN model; COVID-19; accuracy
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28. Rustam F, Reshi AA, Ashraf I, et al. Sensor-based human activity recognition using deep stacked multilayered perceptron model. IEEE Access 2020; 8: 218898–218910. doi: 10.1109/ACCESS.2020.3041822
29. Kaur N, Jindal N, Singh K. A passive approach for the detection of splicing forgery in digital images. Multimed Tools Application 2020; 79(43): 32037–32063. doi: 10.1007/s11042-020-09275-w
30. Kaur N, Jindal N, Singh K. A deep learning framework for copy-move forgery detection in digital images. Multimedia Tools and Applications 2023; 82: 17741–17768. doi: 10.1007/s11042-022-14016-2
31. Kaur N, Jindal N, Singh K. An improved approach for single and multiple copy-move forgery detection and localization in digital images. Multimedia Tools and Applications 2022; 81(27): 38817–38847. doi: 10.1007/s11042-022-13105-6
32. Lau KW, Wu QH. Online training of support vector classifier. Pattern Recognition 2003; 36(8): 1913–1920. doi: 10.1016/S0031-3203(03)00038-4
33. Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics 2002; 35(5–6): 352–359. doi: 10.1016/S1532-0464(03)00034-0
2. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access 2020; 8: 101489–101499. doi: 10.1109/ACCESS.2020.2997311
3. Malhotra P, Gupta S, Koundal D, et al. Deep learning-based computer-aided pneumothorax detection using chest X-ray images. Sensors 2020; 22(6): 2278. doi: 10.3390/s22062278
4. Cennimo DJ. Coronavirus disease 2019 (COVID-19) clinical presentation. Available online: https://emedicine.medscape.com/article/2500114-clinical#b2 (accessed on 1 August 2023).
5. X-ray (Radiography)-Chest, 2020. Available online: https://www.radiologyinfo.org/en/info/chestrad (accessed on 1 November 2022).
6. Sharma S, Gupta S, Gupta D, et al. Performance evaluation of the deep learning based convolutional neural network approach for the recognition of chest X-ray images. Frontiers in Oncology 2020; 12: 932496. doi: 10.3389/fonc.2022.932496
7. Ahmad M. Ground truth labeling and samples selection for hyperspectral image classification. Optik 2021; 230: 166267. doi: 10.1016/j.ijleo.2021.166267
8. Kayalibay B, Jensen G, van der Smagt P. CNN-based segmentation of medical imaging data. arXiv 2017; arXiv:1701.03056. doi: 10.48550/arXiv.1701.03056
9. Li Q, Cai W, Wang X, et al. Medical image classification with convolutional neural network. In: Proceedings of 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV); 10–12 December 2014; Singapore. pp. 844–848.
10. Umer M, Sadiq S, Ahmad M, et al. A novel stacked CNN for malarial parasite detection in the blood smear images. IEEE Access 2020; 8: 93782–93792. doi: 10.1109/ACCESS.2020.2994810
11. Rouhi R, Jafari M, Kasaei S, et al. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications 2015; 42(3): 990–1002. doi: 10.1016/j.eswa.2014.09.020
12. Sharif M, Khan MA, Rashid M, et al. Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. Journal of Experimental & Theoretical Artificial Intelligence 2019; 33(4): 577–599. doi: 10.1080/0952813X.2019.1572657
13. Asada N, Doi K, MacMahon H, et al. Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: Pilot study. Radiology 1990; 177(3): 857–860. doi: 10.1148/radiology.177.3.2244001
14. Katsuragawa S, Doi K. Computer-aided diagnosis in chest radiography. Computerized Medical Imaging and Graphics 2007; 31(4–5): 212–223. doi: 10.1016/j.compmedimag.2007.02.003
15. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115–118. doi: 10.1038/nature21056
16. Dong D, Tang Z, Wang S, et al. The role of imaging in the detection and management of COVID-19: A review. IEEE Reviews in Biomedical Engineering 2020; 14: 16–29. doi: 10.1109/RBME.2020.2990959
17. 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; arXiv:2003.09871. doi: 10.48550/arXiv.2003.09871
18. Apostolopoulos ID, Mpesiana TA. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine 2020; 43: 635–640. doi: 10.1007/s13246-020-00865-4
19. Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv 2020; arXiv:2003.13815. doi: 10.48550/arXiv.2003.13815
20. Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications 2021; 24(3): 1207–1220. doi: 10.1007/s10044-021-00984-y
21. Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked 2020; 20: 100412. doi: 10.1016/j.imu.2020.100412
22. Shorfuzzaman M, Masud M, Alhumyani H, et al. Artificial neural network-based deep learning model for COVID-19 patient detection using X-ray chest images. Journal of Healthcare Engineering 2021; 2021: 5513679. doi: 10.1155/2021/5513679
23. Mooney P. Chest X-ray images (Pneumonia). Available online: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed on 1 August 2023).
24. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. Journal of Big Data 2019; 6: 60. doi: 10.1186/s40537-019-0197-0
25. Ho D, Liang E, Liaw R. 1000x Faster data augmentation. Available online: https://bair.berkeley.edu/blog/2019/06/07/data_aug/ (accessed on 1 August 2023).
26. Mane DT, Kulkarni UV. A survey on supervised convolutional neural network and its major applications. International Journal of Rough Sets and Data Analysis 2017; 4(3): 71–82. doi: 10.4018/IJRSDA.2017070105
27. Liaw A, Wiener M. Classification and regression by randomForest. R News 2002; 2: 18–22.
28. Rustam F, Reshi AA, Ashraf I, et al. Sensor-based human activity recognition using deep stacked multilayered perceptron model. IEEE Access 2020; 8: 218898–218910. doi: 10.1109/ACCESS.2020.3041822
29. Kaur N, Jindal N, Singh K. A passive approach for the detection of splicing forgery in digital images. Multimed Tools Application 2020; 79(43): 32037–32063. doi: 10.1007/s11042-020-09275-w
30. Kaur N, Jindal N, Singh K. A deep learning framework for copy-move forgery detection in digital images. Multimedia Tools and Applications 2023; 82: 17741–17768. doi: 10.1007/s11042-022-14016-2
31. Kaur N, Jindal N, Singh K. An improved approach for single and multiple copy-move forgery detection and localization in digital images. Multimedia Tools and Applications 2022; 81(27): 38817–38847. doi: 10.1007/s11042-022-13105-6
32. Lau KW, Wu QH. Online training of support vector classifier. Pattern Recognition 2003; 36(8): 1913–1920. doi: 10.1016/S0031-3203(03)00038-4
33. Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics 2002; 35(5–6): 352–359. doi: 10.1016/S1532-0464(03)00034-0
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