Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays

TL;DR

This paper demonstrates how deep learning models, especially transfer learning with InceptionV3, can significantly improve the accuracy and interpretability of chest X-ray classification for respiratory diseases, aiding clinical diagnosis.

Contribution

The study introduces an effective transfer learning approach with focal loss and Grad-CAM for better disease classification and interpretability in chest X-ray analysis.

Findings

InceptionV3 achieved a 28% increase in AUC.

Focal loss improved class imbalance handling.

Grad-CAM provided clinically relevant visual explanations.

Abstract

Chest X-rays play a pivotal role in diagnosing respiratory diseases such as pneumonia, tuberculosis, and COVID-19, which are prevalent and present unique diagnostic challenges due to overlapping visual features and variability in image quality. Severe class imbalance and the complexity of medical images hinder automated analysis. This study leverages deep learning techniques, including transfer learning on pre-trained models (AlexNet, ResNet, and InceptionNet), to enhance disease detection and classification. By fine-tuning these models and incorporating focal loss to address class imbalance, significant performance improvements were achieved. Grad-CAM visualizations further enhance model interpretability, providing insights into clinically relevant regions influencing predictions. The InceptionV3 model, for instance, achieved a 28% improvement in AUC and a 15% increase in F1-Score. These findings highlight the potential of deep learning to improve diagnostic workflows and support clinical decision-making.