Lung Infection Detection using Progressive U-NET Architecture

Abstract

The fragmentation of medical images of tissue anomalies, imorgans, or the blood vascular system is critical for any computerized diagnostic system. Nevertheless, automated categorization in clinical visual assessment was a difficult problem as it necessitates in-depth information about the specific organ structure. This article presents UNET, an edge deep learning categorization technique for early recognition of COVID. This is also a problem for treatment and correct intervention, as previous techniques were inappropriate in this situation. Trying to keep this in consideration, non-invasive methods such as CT scans and X-rays were suggested to get characteristics of lungs.