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:: Volume 22, Issue 1 (1-2024) ::
Int J Radiat Res 2024, 22(1): 55-64 Back to browse issues page
A modified deep learning model in the classification of post-COVID-19 lung disease and a comparative study on Iranian and international databases
S. Cheraghi , S. Amiri , F. Abdolali , A. Janati Esfahani , A. Amiri Tehrani Zade , R. Ahadi , F. Ansari , E. Raiesi Nafchi , Z. Hormozi-Moghaddam
Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran , janaty.azam@gmail.com
Abstract:   (532 Views)
Background: We introduced Mask R-CNN+CNN as a deep learning model to classify COVID-19 and non-COVID-19 cases. Radiomic features relevant to COVID-19 was presented for Iranian and other nationalities. Materials and Methods: Chest CT images from 800 COVID-19 positive and negative patients were studied. The automated volume of the lung and segmentation of COVID-19 lung lesions were implemented using 3D U-net, Capsule network, and Mask R-CNN on annotated CT images. Deep learning models designed were based on Mask R-CNN, CNN, and Mask R-CNN+CNN algorithms to classify COVID-19 cases. We also explored radiomic features relevant to the COVID-19 pandemic in the lungs for chest CT images and implemented random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) algorithms on two datasets. Results: The Mask R-CNN+CNN model demonstrated a higher classification accuracy (96.39 ± 2.94) compared to the Mask R-CNN and CNN models. The RF algorithm had greater power in differentiating relevant COVID-19 radiomic features compared to DT and GBDT, with an accuracy of at least 91 and an AUC of at least 985 in both datasets. We identified six radiomic features that were relevant to the pathological characteristics of COVID-19 positive/negative patients and were common across all datasets. Conclusion: This study emphasizes the power of Mask R-CNN+CNN with a ResNet-101 backbone as a CNN algorithm that utilizes bounding box offsets output from Mask R-CNN as the input for classifying COVID-19 cases. Radiomic features extracted from lung CT images might aid the diagnosis of COVID-19 in patients at various stages of the disease.
Keywords: Machine learning, COVID-19, Computed tomography, Mask R-CNN+CNN, Deep learning.
Full-Text [PDF 813 kb]   (214 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. 1. Laino ME, Ammirabile A, Posa A, et al. (2021) The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics, 11(8): 1317. [DOI:10.3390/diagnostics11081317]
2. Wang G, Zheng X, Zou Y, et al. (2023) CT imaging findings and clinical correlation analysis of different clinical types of novel coronavirus pneumonia %J. Int J Radiat Res, 21(1): 7-13.
3. Malguria N, Yen LH, Lin T, et al. (2020) Hussein A, Fishman EK. Role of Chest CT in COVID-19. J Clin Imaging Sci, 2021(11): 30. [DOI:10.25259/JCIS_138_2020]
4. Li Y, Yao L, Li J, et al. (2020) Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19. J Med Virol, 92(7): 903-8. [DOI:10.1002/jmv.25786]
5. Santura I, Kawalec P, Furman M, Bochenek T (2021) Chest computed tomography versus RT-PCR in early diagnostics of COVID-19 - a systematic review with meta-analysis. Pol J Radiol, 86: e518-e31. [DOI:10.5114/pjr.2021.109074]
6. Xie X, Zhong Z, Zhao W, et al. (2020) Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing. Radiology, 296(2): E41-E5. [DOI:10.1148/radiol.2020200343]
7. Jain G, Mittal D, Thakur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybernetics and Biomedical Engineering, 40(4):1391-405. [DOI:10.1016/j.bbe.2020.08.008]
8. Zu ZY, Jiang MD, Xu PP, et al. (2020) Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology, 296(2): E15-E25. [DOI:10.1148/radiol.2020200490]
9. Cai W, Liu T, Xue X, et al. (2020) CT quantification and machine-learning models for assessment of disease severity and prognosis of COVID-19 patients. Academic radiology.;27(12):1665-78. [DOI:10.1016/j.acra.2020.09.004]
10. Sperrin M, Grant SW, Peek N (2020) Prediction models for diagnosis and prognosis in Covid-19. British Medical Journal Publishing Group; 2020, 369. [DOI:10.1136/bmj.m1464]
11. Chung M, Bernheim A, Mei X, et al. (2020) CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1): 202-7. [DOI:10.1148/radiol.2020200230]
12. Zhu Y, Gao Z-H, Liu Y-L, et al. (2020) Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19). The Journal of Infection, 81(1): 147. [DOI:10.1016/j.jinf.2020.03.033]
13. Shi H, Han X, Jiang N, et al. (2020) Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet Infectious Diseases, 20(4): 425-34. [DOI:10.1016/S1473-3099(20)30086-4]
14. Ye Z, Zhang Y, Wang Y, et al. (2020) Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. European Radiology, 30(8): 4381-9. [DOI:10.1007/s00330-020-06801-0]
15. Ling Z, Xu X, Gan Q, et al. (2020) Asymptomatic SARS-CoV-2 infected patients with persistent negative CT findings. European Journal of Radiology, 126. [DOI:10.1016/j.ejrad.2020.108956]
16. Liu R, Han H, Liu F, et al. (2020) Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clinica Chimica Acta, 505: 172-5. [DOI:10.1016/j.cca.2020.03.009]
17. Zhang H-t, Zhang J-s, Zhang H-h, et al. (2020) Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. European Journal of Nuclear Medicine and Molecular Imaging, 47(11): 2525-32. [DOI:10.1007/s00259-020-04953-1]
18. Litjens G, Kooi T, Bejnordi BE, et al. (2017) A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60-88. [DOI:10.1016/j.media.2017.07.005]
19. Panch T, Szolovits P, Atun R (2018) Artificial intelligence, machine learning and health systems. Journal of global health, 8(2). [DOI:10.7189/jogh.08.020303]
20. Amiri S, Akbarabadi M, Abdolali F, et al. (2021) Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models. Computers in Biology and Medicine, 133: 104409. [DOI:10.1016/j.compbiomed.2021.104409]
21. Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139: 110059. [DOI:10.1016/j.chaos.2020.110059]
22. Sear RF, Velásquez N, Leahy R, et al. (2020) Quantifying COVID-19 content in the online health opinion war using machine learning. Ieee Access, 8: 91886-93. [DOI:10.1109/ACCESS.2020.2993967]
23. Maghdid HS, Asaad AT, Ghafoor KZ, et al. (2021) editors. Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. Multimodal Image Exploitation and Learning 2021. International Society for Optics and Photonics. [DOI:10.1117/12.2588672]
24. Swapnarekha H, Behera HS, Nayak J, Naik B (2020) Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons & Fractals, 138: 109947. [DOI:10.1016/j.chaos.2020.109947]
25. Neri E, Miele V, Coppola F, Grassi R (2020) Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology. La Radiologia Medica, 125(5): 505-8. [DOI:10.1007/s11547-020-01197-9]
26. Grassi R, Belfiore MP, Montanelli A, et al. (2021) COVID-19 pneumonia: Computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT). La Radiologia Medica, 126(4): 553-60. [DOI:10.1007/s11547-020-01305-9]
27. Santos MK, Ferreira JR, Wada DT, et al. (2019) Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52: 387-96. [DOI:10.1590/0100-3984.2019.0049]
28. Scapicchio C, Gabelloni M, Barucci A, et al. (2021) A deep look into radiomics. La Radiologia Medica, 126(10):1296-311. [DOI:10.1007/s11547-021-01389-x]
29. Lee Y, Hwang J-w, Lee S, Bae Y, Park J (2019) editors. An energy and GPU-computation efficient backbone network for real-time object detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops; 2019. [DOI:10.1097/GME.0000000000001240]
30. Jiménez-Sánchez A, Albarqouni S, Mateus D (2018) Capsule networks against medical imaging data challenges. Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis: Springer, 2018: 150-60. [DOI:10.1007/978-3-030-01364-6_17]
31. Ramesh V, Rister B, Rubin DL (2021) Covid-19 lung lesion segmentation using a sparsely supervised mask R-CNN on chest x-rays automatically computed from volumetric CTS. ArXiv preprint. arXiv, 210508147.
32. He X, Zhou C, Wang Y, Yuan X (2021) Risk assessment and prediction of COVID-19 based on epidemiological data from spatiotemporal geography. Frontiers in Environmental Science, 9: 634156. [DOI:10.3389/fenvs.2021.634156]
33. Muhammad L, Algehyne EA, Usman SS, et al. (2021) Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN Computer Science, 2: 1-13. [DOI:10.1007/s42979-020-00394-7]
34. Tuli S, Tuli S, Tuli R, Gill SS (2020) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11: 100222. [DOI:10.1016/j.iot.2020.100222]
35. Song Y, Zheng S, Li L, et al. (2021) Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6): 2775-80. [DOI:10.1109/TCBB.2021.3065361]
36. Ardakani AA, Kanafi AR, Acharya UR, et al. (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in Biology and Medicine, 121: 103795. [DOI:10.1016/j.compbiomed.2020.103795]
37. Liu B, Yan B, Zhou Y, et al. (2020) Experiments of federated learning for covid-19 chest X-ray images. ArXiv Preprint arXiv: 200705592.
38. Li Z, Peng C, Yu G, et al. (2018) Detnet: A backbone network for object detection. arXiv preprint arXiv, 180406215.
39. He K, Zhang X, Ren S, Sun J (2016) editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. Publisher: IEEE. [DOI:10.1109/CVPR.2016.90]
40. Podder S, Bhattacharjee S, Roy A (2021) An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images. AIMS Biophys, 8: 281-90. [DOI:10.3934/biophy.2021022]
41. Ter-Sarkisov A (2022) Covid-ct-mask-net: Prediction of covid-19 from ct scans using regional features. Applied Intelligence. 2022:1-12. [DOI:10.1007/s10489-021-02731-6]
42. Haiming T, Nanfei S, Yi L (2020) Segmentation model of the opacity regions in the chest X-rays of the Covid-19 patients in the us rural areas and the application to the disease severity. medRxiv, 10(19): 20215483.
43. Chen H, Ai L, Lu H, Li H (2020) Clinical and imaging features of COVID-19. Radiology of Infectious Diseases, 7(2): 43-50. [DOI:10.1016/j.jrid.2020.04.003]
44. Haque KF, Haque FF, Gandy L, Abdelgawad A (2020) editors. Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks. 2020 international conference on computing, electronics & communications engineering (iCCECE). IEEE.
45. Gunraj H, Sabri A, Koff D, Wong A (2022) COVID-Net CT-2: Enhanced deep neural networks for detection of covid-19 from chest ct images through bigger, more diverse learning. Front Med, 8: 729287. [DOI:10.3389/fmed.2021.729287]
46. Mahmud T, Rahman MA, Fattah SA (2020) 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. Computers in Biology and Medicine, 122: 103869. [DOI:10.1016/j.compbiomed.2020.103869]
47. Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Scientific Reports, 10(1): 19549. [DOI:10.1038/s41598-020-76550-z]
48. Kundu A, Mishra C, Bilgaiyan S (2021) editors. Covid-segnet: Diagnosis of covid-19 cases on radiological images using mask r-cnn. 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII). IEEE. [DOI:10.1109/ICBSII51839.2021.9445190]
49. Toraman S, Alakus TB, Turkoglu I (2020) Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons & Fractals, 140: 110122. [DOI:10.1016/j.chaos.2020.110122]
50. Ke G, Meng Q, Finley T, et al. (2017) editors. LightGBM: A highly efficient gradient boosting decision tree. NIPS; 2017. [DOI:10.1515/9780824864613-022]
51. Shamout FE, Shen Y, Wu N, et al. (2021) An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. npj Digital Medicine, 4(1): 80. [DOI:10.1038/s41746-021-00453-0]
52. Tang Z, Zhao W, Xie X, et al. (2020) Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images. Phys Med Biol, 66: 035015 [DOI:10.1088/1361-6560/abbf9e]
53. Fu L, Li Y, Cheng A, et al. (2020) A novel machine learning-derived radiomic signature of the whole lung differentiates stable from progressive COVID-19 infection: A retrospective cohort study. J Thorac Imaging, 35(6): 361-8. [DOI:10.1097/RTI.0000000000000544]
54. Wu Z, Li L, Jin R, et al. (2021) Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19. Eur J Radiol, 137:109602. [DOI:10.1016/j.ejrad.2021.109602]
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Cheraghi S, Amiri S, Abdolali F, Janati Esfahani A, Amiri Tehrani Zade A, Ahadi R, et al . A modified deep learning model in the classification of post-COVID-19 lung disease and a comparative study on Iranian and international databases. Int J Radiat Res 2024; 22 (1) :55-64
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Volume 22, Issue 1 (1-2024) Back to browse issues page
International Journal of Radiation Research
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