CHASHNIt: Enhancing Skin Disease Classificationleveraging GAN-Augmented Hybrid Model withComparative XAI Interpretation Heatmap Using LIMEand SHAP Algorithms

Correct categorization of skin diseases is vital for prompt diagnosis. However, obstacles such as imbalance of data andinterpretability of deep learning models limit their use in medical settings. To overcome these setbacks, Combined Hybrid Architecture for Scalable High-performance in Neural Iterations or CHASHNIt is proposed, which is an integration of EfficientNetB7,DenseNet201, and InceptionResNetV2 to outperform current models on every ground. GAN-based data augmentation is usedto create synthetic images, to ensure that all classes are equally represented. Sophisticated preprocessing methods such asnormalization and feature selection improve data quality and model generalization. Explainable AI methods, i.e., SHAP andLIME, enable model decision-making transparent. A rigorous comparative analysis testifies to the excellence of CHASHNItcompared to other benchmark models with 97.8% accuracy, 98.1% precision, 97.5% recall, 97.6% F1 Score and IoU of 92.3%,which exceeds Swin Transformer, ResNet101, InceptionResNetV2, MobileNetV3, EfficientNetB7, DenseNet201, and ConvNeXtmodels. The model was trained and tested on a 19,500-image dataset of 23 types of skin diseases with 80:20 split for trainingand testing. An ablation study testifies to the synergy advantage of the hybrid approach. LIME-SHAP heatmaps confirm themodel’s predictive result. CHASHNIt is a revolutionary leap in the classification of skin diseases, attaining a balance betweenscalability, accuracy, and explainability. Computational complexity is the sole drawback, but future developments will optimizeefficiency for low-resource devices.