Multi-Classification Convolution Neural Network Models for Chest Disease Classification

Chest diseases significantly affect public health, causing more than one million hospital admissions and approximately 50,000 deaths annually in the United States. Chest X-ray imaging technology, which is a critically important imaging technique, helps in examining, diagnosing, and managing chest conditions by providing essential insights about the presence and severity of disease. This study introduces a novel chest X-ray classification framework leveraging a fine-tuned VGG19 model (16 layers) enhanced with CLAHE for improved contrast, binary mask attention to highlight abnormalities and advanced data augmentation for better generalization. Key innovations include the use of a Probabilistic U-Net for lung segmentation to isolate critical features and weighted masks to focus on pathological regions, addressing class imbalance with computed class weights for fair learning. By achieving 95% accuracy and superior class-specific metrics, the proposed method outperforms existing deep learning approaches, providing a robust and interpretable solution for real-world healthcare applications, where a test accuracy of 94.8% is achieved using different customized models based on VGG19 without using a mask. The experimental results indicate that our proposed method surpasses current deep learning techniques in terms of overall classification accuracy for chest disease detection.

@article{Ayman2025,
  title     = {Multi-Classification Convolution Neural Network Models for Chest Disease Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Noha Ayman and Mahmoud E. A. Gadallah and Mary Monir Saeid},
  doi       = {10.14569/IJACSA.2025.0160240},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160240}
}