Novel Architecture for Image Classification Based on Rough Set

The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.