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 quali ̄ed doctors for medical images-based diagnosis. Recent numerous advancements in arti ̄cial intel- ligence 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 abnor- mality 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 enlarge- ment, cardiomegaly, lung opacity, pleural e®usion, and pulmonary ̄brosis).