XAI-SkinCADx: A Six-Stage Explainable Deep Ensemble Framework for Skin Cancer Diagnosis and Risk-Based Clinical Recommendations

The fastest growing and deadliest cancer, skin cancer, requires diagnostic solutions that are accurate, understandable, and therapeutically actionable. Our six-stage deep ensemble diagnostic framework, XAI-SkinCADx, employs hybrid feature extraction, deep learning ensembles, understandable AI, and clinical recommendation creation to meet this need. The 2367 dermoscopy images belonging to nine distinct classes of the ISIC skin cancer dataset are used to train the framework. Adding 5,023 new images makes the dataset richer and better balanced across the classes. A comprehensive performance evaluation is ensured by using the dataset with an 80:20 train-data/test-data split. It begins with hand-designed feature extraction using GLCM-based dimensionality reduction and visualization and Local Binary Patterns (LBP) visualization. Three convolutional neural networks are then employed to extract specific spatial features: DV-25 (DenseNet201 + VGG16), DE-25 (DenseNet201 + EfficientNetB5), and DM-25 (DenseNet201 + MobileNetV2). Temporal patterns are described with bidirectional long-short-term memory (BiLSTM) and finally classified with multiclass SVM. The accuracy is 95.63%, precision is 96.2%, recall is 95.7%, F1 score is 95.9%, and area under curve is 0.97. We interpret it using Grad-CAM++ and LIME. Compared to Grad-CAM++, LIME had better localization accuracy (IoU: 0.78, Dice: 0.87) while Grad-CAM++ had worse accuracy (IoU: 0.70, Dice: 0.81). The best performing classes with LIME interpretability, Vascular Lesion, Seborrhoeic Keratosis, and Dermatofibroma, respectively, with Dice scores of 0.91, 0.89, and 0.87 respectively. For providing patients with personalized suggestions independent of other information, a dermatology-focused LIME-based recommendation platform was designed. The timely dermatology consultation and decreased clinical burden are led by the classification by the system of the disorders as low-risk, medium-risk, and high-risk. By integrating accuracy, clarity, and utility, XAI-SkinCADx creates a new benchmark for applying AI in the diagnosis of skin disease, enabling physicians to make well-informed and trusted decisions.