Deep learning classification of chest x-ray images

TL;DR

This paper presents a deep learning approach for classifying common chest X-ray pathologies, demonstrating improved detection accuracy over existing methods using publicly available data.

Contribution

The paper introduces a novel deep learning method for chest X-ray classification and compares its performance to existing approaches, showing improved accuracy.

Findings

Improved AUC for nodule detection

Enhanced accuracy for cardiomegaly classification

Utilized publicly available chest X-ray datasets

Abstract

We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.