Lung disease classification using NF-RVFL optimised by IROA with HybridGNet segmentation and DIAG augmented data in chest X-Ray analysis

Lung diseases like lung opacity, pneumonia, and COVID-19 pose major health challenges, creating a need for more advanced diagnostic solutions. Existing methods suffer from high space requirements, data imbalance, long processing times, and deep learning (DL) limitations like vanishing gradients and increased misclassification. To solve this, a novel lung disease classification framework is proposed that integrates data augmentation, segmentation, and classification. The diffusion-based In-Distribution Anomaly Generation (DIAG) pipeline uses multimodal latent diffusion models to generate realistic anomalous images, effectively resolving data imbalance. For segmentation, HybridGNet model combines convolutional neural networks (CNNs) with graph convolutional neural networks to accurately decode anatomical structures. Classification is performed using neuro-fuzzy random vector functional link (NF-RVFL) model, which employs an interpretable IF-THEN decision structure. Its parameters are optimised using Improved Remora Optimization Algorithm, reducing complexity and enhancing performance. Experimental results show an accuracy of 0.989, outperforming existing methods and demonstrating strong potential for precise lung disease classification.