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International Journal of Radiation Research، جلد ۲۲، شماره ۱، صفحات ۵۵-۶۴
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عنوان انگلیسی A modified deep learning model in the classification of post-COVID-19 lung disease and a comparative study on Iranian and international databases
چکیده انگلیسی مقاله
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.
کلیدواژه‌های انگلیسی مقاله Machine learning, COVID-19, Computed tomography, Mask R-CNN+CNN, Deep learning.
نویسندگان مقاله | S. Cheraghi
Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran


| S. Amiri
Department of Computer Sciences, University of Copenhagen, Copenhagen, Denmark


| F. Abdolali
Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, Alberta University, Edmonton, AB, Canada


| A. Janati Esfahani
Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran


| A. Amiri Tehrani Zade
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran


| R. Ahadi
Department of Anatomical Science Iran University of Medical Science Tehran, Iran


| F. Ansari
Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran


| E. Raiesi Nafchi
Department of Radiation Sciences, Allied Medicine Faculty, Iran University of Medical Sciences, Tehran, Iran


| Z. Hormozi-Moghaddam
Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
نشانی اینترنتی http://ijrr.com/browse.php?a_code=A-10-1-1138&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Radiation Biology
نوع مقاله منتشر شده تحقیق بدیع
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