International Journal of Radiation Research

en A modified deep learning model in the classification of post-COVID-19 lung disease and a comparative study on Iranian and international databases Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;">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.</div> Machine learning, COVID-19, Computed tomography, Mask R-CNN+CNN, Deep learning. 55 64 http://ijrr.com/browse.php?a_code=A-10-1-1138&slc_lang=en&sid=1 S. Cheraghi 7900319475328460025860 7900319475328460025860 No Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran S. Amiri 7900319475328460025861 7900319475328460025861 No Department of Computer Sciences, University of Copenhagen, Copenhagen, Denmark F. Abdolali 7900319475328460025862 7900319475328460025862 No Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, Alberta University, Edmonton, AB, Canada A. Janati Esfahani janaty.azam@gmail.com 7900319475328460025863 7900319475328460025863 Yes Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran A. Amiri Tehrani Zade 7900319475328460025864 7900319475328460025864 No Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran R. Ahadi 7900319475328460025865 7900319475328460025865 No Department of Anatomical Science Iran University of Medical Science Tehran, Iran F. Ansari 7900319475328460025866 7900319475328460025866 No Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran E. Raiesi Nafchi 7900319475328460025867 7900319475328460025867 No Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran Z. Hormozi-Moghaddam 7900319475328460025868 7900319475328460025868 No Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran