GIL-CNN: A Novel Multipath Features for COVID-19 Detection Using CT-Scan Images

The rapid expansion of coronavirus illness (COVID-19) became a global problem, with over 40 million confirmed individuals infected as of February 2022. Medical imaging, such as CT scans, can be used for diagnostics to counteract its spread. Automatic COVID detection tools are required to ease the process using CT scan images. This work categorizes the healthy and COVID patients based on CT scan images. As such, a GIL-CNN model is proposed to detect COVID patients in the present paper. The proposed model provides the global, intermediate, and local features that help better CT scan image feature representation by avoiding spatial loss. The support vector machine, a standard machine learning classifier, is used to classify the normal and COVID images using the GIL-CNN features. The proposed model is trained and tested on publicly available COVID-19 CT scan images. The proposed model has produced better results than state-of-the-art techniques. The proposed model can assist in the automated identification of COVID-19 patients, reducing the strain on healthcare systems.