Chest X-ray Abnormalities Detection using Deep learning

Abstract

Thorax illness is a serious medical problem. Approximately 3 million individuals die each year from thoracic disorders, according to a recent report [10]. Inspection of medical images demands strong degree of professional experience and focus. Furthermore, it also takes a lot of time and costly. As a result, it is critical to automate the diagnosis of thorax disorders using chest radiography. In this study, we used an ensemble strategy to detect anomalies in chest radiographs having limited image size of 512x512, we also utilized two class filters over the predictions of our two models i.e. Yolov5 and faster R-CNN, and applied augmentations as well. The implemented methodology was tested using the VinBigData Chest X-ray Abnormalities Detection Competition [11] evaluation method, and it received one of the top score in the challenge.