Identification of COVID-19 with CT scans using radiomics and DL-based features

Deep learning plays a crucial role in identifying COVID-19 patients from computed tomography (CT) scans by leveraging its ability to analyze vast amounts of image data and extract patterns indicative of the disease. While deep learning-based models have consistently achieved state-of-the-art performance, the incorporation of relevant handcrafted features alongside deep learning-based features has the potential to enhance overall performance even further. Therefore, this paper proposes a hybrid approach that combines handcrafted and deep learning features from CT scan images for accurate COVID-19 classification. Handcrafted features capturing image statistics are derived through radiomics, while deep learning features are extracted using the Xception model. Preprocessing techniques like binary thresholding and segmentation are used to remove noises and locate the proper diseased area to enhance COVID-19 diagnosis. The approach is evaluated on a dataset of 2482 CT scan images and outperforms state-of-the-art techniques with an accuracy of 0.98, a positive predictive value (PPV) of 0.99, sensitivity of 0.99, specificity of 0.98, and an F_1 -score of 0.99. The combined use of radiomics and deep learning features can make it a promising tool for COVID-19 diagnosis and monitoring, offering support for clinical decision-making and potentially benefiting other respiratory diseases.