Background: Radiomic features have been extensively used with machine learning and other Artificial Intelligence methods in medical imaging problems. Coronavirus Disease 2019 (COVID-19), which has been spreading worldwide since 2020, has motivated scientists to develop automatic COVID-19 recognition systems, to enhance the clinical
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Background: Radiomic features have been extensively used with machine learning and other Artificial Intelligence methods in medical imaging problems. Coronavirus Disease 2019 (COVID-19), which has been spreading worldwide since 2020, has motivated scientists to develop automatic COVID-19 recognition systems, to enhance the clinical routine in overcrowded hospitals. Purpose: To develop an automated system of recognizing COVID-19 and Community-Acquired Pneumonia (CAP) using radiomic features extracted from whole lung chest Computed Tomography (CT) images. Radiomic feature extraction from whole lung CTs simplifies the image segmentation for the malignancy region of interest (ROI).
Methods: In this work, we used radiomic features extracted from CT images representing whole lungs to train various machine learning models that are capable of identifying COVID-19 images, CAP images and healthy cases. The CT images were derived from an open access data set, called COVID-CT-MD, containing 76 Normal cases, 169 COVID-19 cases and 60 CAP cases. Results: Four two-class models and one three-class model were developed: Normal–COVID, COVID–CAP, Normal–CAP, Normal–Disease and Normal–COVID–CAP. Different algorithms and data augmentation were used to train each model 20 times on a different data set split, and, finally, the model with the best average performance was selected for each case. The performance metrics of Accuracy, Sensitivity and Specificity were used to assess the performance of the different systems. Since COVID-19 and CAP share similar characteristics, it is challenging to develop a model that can distinguish these diseases.
Result: The results were promising for the models finally selected for each case. The accuracy for the independent test set was 83.11% in the Normal–COVID case, 88.77% in the COVID–CAP case, 93.97% in the Normal–CAP case and 94.13% in the Normal–Disease case, when referring to two-class cases, while, in the three-class case, the accuracy was 78.55%.
Conclusion: The results obtained suggest that radiomic features extracted from whole lung CT images can be successfully used to distinguish COVID-19 from other pneumonias and normal lung cases.
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