Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays
This work addresses diagnostic challenges for clinicians in detecting respiratory diseases like pneumonia and COVID-19, but it is incremental as it applies existing methods to a specific medical domain.
This study tackled the problem of classifying thoracic diseases in chest X-rays, which is hindered by class imbalance and image complexity, by using deep learning with transfer learning and focal loss, resulting in a 28% improvement in AUC and a 15% increase in F1-Score for the InceptionV3 model.
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.