A Convolutional Neural Network Model for Detecting COVID-19 from CT Scans

Using deep learning approaches, this work presents a fully automated system for diagnosing COVID-19 from volumetric chest computed tomography (CT) scans. Transfer learning technique has been used to detect and classify CT scan data into three categories: COVID-19, CAP (Community-acquired pneumonia), and normal cases. The proposed model was built on top of the pre-trained AlexNet model's architecture and was capable of performing multi-classification tasks with a promising accuracy of 98.03%. The results demonstrate that the proposed model outperforms other current models and may thus be utilized as a potential tool for COVID-19 patient diagnosis.