HybridFusionNet with Explanability: A Novel Explainable Deep Learning-Based Hybrid Framework for Enhanced Skin Lesion Classification Using Dermoscopic Images

Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy. Traditional diagnostic methods often rely on subjective visual assessments, which can lead to misdiagnosis. This study addresses these challenges by developing HybridFusionNet, a novel model that integrates Convolutional Neural Networks (CNN) with 1D feature extraction techniques to enhance diagnostic accuracy. Utilizing two extensive datasets, BCN20000 and HAM10000, the methodology includes data preprocessing, application of Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors (SMOTEENN) for data balancing, and optimization of feature selection using the Tree-based Pipeline Optimization Tool (TPOT). The results demonstrate significant performance improvements over traditional CNN models, achieving an accuracy of 0.9693 on the BCN20000 dataset and 0.9909 on the HAM10000 dataset. The HybridFusionNet model not only outperforms conventional methods but also effectively addresses class imbalance. To enhance transparency, it integrates post-hoc explanation techniques such as LIME, which highlight the features influencing predictions. These findings highlight the potential of HybridFusionNet to support real-world applications, including physician-assist systems, teledermatology, and large-scale skin cancer screening programs. By improving diagnostic efficiency and enabling access to expert-level analysis, the model may enhance patient outcomes and foster greater trust in artificial intelligence (AI)-assisted clinical decision-making.