Chest Diseases Classification Using CXR and Deep Ensemble Learning

Abstract

Chest diseases are among the most common worldwide health problems; they are potentially life-threatening disorders which can affect organs such as lungs and heart. Radiologists typically use visual inspection to diagnose chest X-ray (CXR) diseases, which is a difficult task prone to errors. The signs of chest abnormalities appear as opacities around the affected organ, making it difficult to distinguish between diseases of superimposed organs. To this end, we propose a very first method for CXR organ disease detection using deep learning. We used an ensemble learning (EL) approach to increase the efficiency of the classification of CXR diseases by organs (lung and heart) using a consolidated dataset. This dataset contains 26,316 CXR images from VinDr-CXR and CheXpert datasets. The proposed ensemble of deep convolutional neural networks (DCNN) approach achieves excellent performance with an AUC of 0.9489 for multi-class classification, outperforming many state-of-the-art models.