Automated Pneumonia Detection Method Using Hybrid Semantic Segmentation Network

Abstract

Pneumonia is a potentially life-threatening condition that causes the air sacs in the lungs to become full of pus or fluid. Pneumonia is one of the lung infection diseases that can sometimes lead to severe or life-threatening illness and even death. Chest X-rays are mainly used for the diagnosis of pneumonia. Early detection of pneumonia is a challenge in the X-ray imaging due to the limited color scheme of X-ray imaging. Another major drawback in the early diagnosis of pneumonia is the human-dependent detection. Thus, it is the need of the hour to diagnose pneumonia at an early stage. Inspired by this issue, in this work, a novel hybrid semantic segmentation network is proposed for early detection and classification of pneumonia. Various performance metrics have been used to analyze the performance of the proposed network. Experimental results prove the efficiency of the hybrid semantic segmentation network compared with the other existing approaches in the recent works.