Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays
Journal:
arXiv
Published Date:
Jan 24, 2025
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.
