EnsembleSkinNet: a transfer learning-based framework for efficient skin cancer detection with explainable AI...

Skin cancer remains to be one of the commonest and life-threatening cancers in the world, early diagnosis is vital for an effective treatment. Recent advances in deep learning, specifically Convolutional Neural Networks (CNNs), have led to remarkable progress in skin lesion classification by artificial intelligence. Although single CNN-based methods have been shown to provide high accuracies on their specific datasets and patient conditions, variations in lesion morphology, image quality, and acquisition settings can limit the generalization of these methods on a new unseen dataset. To overcome these difficulties, we present EnsembleSkinNet which is more explainable ensemble deep learning framework for skin image classification based on a softmax-weighted spectrum fusion of different pre-trained CNN architectures including Modified VGG16 (M-VGG16), ResNet50, Inception V3 and DenseNet201. Our framework improves the robustness and reliability by using transfer learning, fine-tuning, and Bayesian hyperparameter optimization for classification. For experimental evaluation using five-fold cross-validation in HAM10000 dataset, it achieves an accuracy of 98.32 ± 0.41%, precision = 98.20 ± 0.35%, recall = 98.10 ± 0.38%, and F1-score = 98.15 ± 0.37%. Cross-domain generalization was also demonstrated by obtaining an excellent external validation accuracy of 96.84 ± 0.42% and AUC = 0.983 on the ISIC 2020 dataset. Additionally, the Grad-CAM-based explainability analysis reached a mean Explainability Accuracy of 93.6% (k = 0.87), indicating agreement with dermatologist annotations. Clinically, this means that false negatives for melanoma and basal cell carcinoma were measurable less, so more cases are caught early and diagnostically driven confidence improved. Hence, EnsembleSkinNet presents a simple, reproducible, interpretable, and clinically adopted framework for OA compliant robust AI-based skin cancer diagnosis.