TY - JOUR

T1 - A modified deep learning model in the classification of post-COVID-19 lung disease and a comparative study on Iranian and international databases

AU - Cheraghi, S.

AU - Amiri, S.

AU - Abdolali, F.

AU - Esfahani, A. Janati

AU - Zade, A. Amiri Tehrani

AU - Ahadi, R.

AU - Ansari, F.

AU - Nafchi, E. Raiesi

AU - Hormozi-Moghaddam, Z.

N1 - Publisher Copyright: © 2024 Novin Medical Radiation Institute. All rights reserved.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Computed tomography

KW - COVID-19

KW - Deep learning

KW - Machine learning

KW - Mask R-CNN+CNN

U2 - 10.52547/ijrr.21.1.9

DO - 10.52547/ijrr.21.1.9

M3 - Journal article

AN - SCOPUS:85186750716

VL - 22

SP - 55

EP - 64

JO - Iranian Journal of Radiation Research

JF - Iranian Journal of Radiation Research

SN - 1728-4554

IS - 1

ER -