An Interpretable Multimodal Approach for Skin Cancer Analysis via Simulated Hyperspectral Reconstruction and Hybrid Machine Learning

Abstract: Skin cancer diagnosis requires accurate lesion classification and reliable stage estimation to enable timely clinical intervention. This study proposes a stage-aware multimodal framework integrating simulated hyperspectral reconstruction with hybrid deep learning and machine learning techniques. Initially, RGB dermoscopic images are preprocessed and enhanced, followed by feature-level class balancing using SMOTE. Principal Component Analysis (PCA) is employed to generate simulated hyperspectral representations, enriching spectral information beyond conventional RGB imaging. Deep semantic features are extracted using a Convolutional Neural Network (CNN), while clinically significant handcrafted features—including tumor diameter, asymmetry index, circularity, and entropy—are computed to capture morphological characteristics. These features are fused and classified using a Support Vector Machine (SVM) for tumor detection, followed by Artificial Neural Network (ANN)-based stage estimation. Experimental results demonstrate that the proposed hybrid framework achieves superior performance, with improved classification accuracy, enhanced robustness under class imbalance, and better generalization compared to single-model approaches. Furthermore, a stage-aware clinical recommendation module provides interpretable and actionable diagnostic insights, enhancing real-world applicability. The proposed system offers a cost-effective and explainable solution for intelligent skin cancer diagnosis. Keyword: Skin Cancer Detection, Simulated Hyperspectral Imaging, Hybrid Deep Learning, CNN-SVM Model, Stage Estimation, Feature Fusion, Clinical Decision Support, SMOTE