Framework for enhanced respiratory disease identification with clinical handcrafted features.

Respiratory disorders cause approximately 4 million deaths annually worldwide, making them the third leading cause of mortality. Early detection is critical to improving survival rates and recovery outcomes. However, chest X-rays require expertise, and computational intelligence provides valuable support to improve diagnostic accuracy and support medical professionals in decision-making. This study presents an automated system to classify respiratory diseases using three diverse datasets comprising 18,000 chest X-ray images and masks, categorized into six classes. Image preprocessing techniques, such as resizing for input standardization and CLAHE for contrast enhancement, were applied to ensure uniformity and improve the visual quality of the images. Albumentations-based augmentation methods addressed class imbalances, while bitwise segmentation focused on extracting the region of interest (ROI). Furthermore, clinically handcrafted feature extraction enabled the accurate identification of 20 critical clinical features essential for disease classification. The K-nearest neighbors (KNN) graph construction technique was utilized to transform tabular data into graph structures for effective node classification. We employed feature analysis to identify critical attributes that contribute to class predictions within the graph structure. Additionally, the GNNExplainer was utilized to validate these findings by highlighting significant nodes, edges, and features that influence the model's decision-making process. The proposed model, Chest X-ray Graph Neural Network (CHXGNN), a robust Graph Neural Network (GNN) architecture, incorporates advanced layers, batch normalization, dropout regularization, and optimization strategies. Extensive testing and ablation studies demonstrated the model's exceptional performance, achieving an accuracy of 99.56 %. Our CHXGNN model shows significant potential in detecting and classifying respiratory diseases, promising to enhance diagnostic efficiency and improve patient outcomes in respiratory healthcare.