VinBigData Chest X-ray Abnormalities Detection is a computer vision project aimed at developing a model that can automatically detect abnormalities in chest Xrays. The project is part of a competition organized by VinBigData, a Vietnamese company focused on developing AI technologies for healthcare.
The dataset used for the project contains over 18,000 chest X-rays, with labels indicating the presence or absence of 14 different abnormalities. The abnormalities include Aortic enlargement, Atelectasis, Calcification, Cardiomegaly, Consolidation, ILD, Infiltration, Lung Opacity, Nodule/Mass, Other lesion, Pleural effusion, Pleural thickening, Pneumothorax, Pulmonary fibrosis.
The YOLOv5 object detection model developed by Ultralytics is utilised in this project to identify abnormalities in chest radiographs. The model is trained on a subset of the total number of independently labelled images and validated on a set of test images, with a split ratio of 80% for the training set and 20% for the validation set. The accuracy and precision are used to assess its performance of the model's results, as well as its limitations and prospective enhancements.