Anomalies Detection in Chest X-Rays Images Using Faster R-CNN and YOLO

Abstract

Lungs are crucial parts of the human body and can be captured as Chest x-ray images for disease diagnosis. Unfortunately, in many countries, hospitals and healthcare centers lack qualified doctors for medical images-based diagnosis. Recent numerous advancements in artificial intelligence have deployed with many medical applications to support doctors for disease diagnosis. In our research, we have leveraged YOLOv5s to identify and extract lungs and performed segmentation tasks with Fast R-CNN and YOLOv5 for comparison. The lung region abnormality detection models have pretty good average precision. For example, the YOLOv5 model outperforms both in terms of training time, prediction, and accuracy, with the AP@.5 and AP@.5:.95 metric values, 0.616 and 0.322 on 2,500 images of 5 abnormalities (aortic enlargement, cardiomegaly, lung opacity, pleural effusion, and pulmonary fibrosis).