XAI-SkinNet: Leveraging Model Ensembles and Explainable AI for Skin Lesion Detection

Skin cancer is the most prevalent cancer globally, with early diagnosis essential in order to decrease mortality. Dermoscopy is commonly used in diagnosis but is time-consuming and subject to variability when it is done manually. This work presents an automated, ensemble-based deep learning system—XAI- SkinNet—to classify skin lesions on the basis of the ISIC 2019 dataset consisting of more than 25,000 dermoscopic images and patient metadata. The system combines three pre-trained convolutional networks: EfficientNetB7, InceptionV3, and ResNeXt, trained separately and together to achieve maximum classification performance. The system uses a two-stream architecture: one stream using images in isolation and another stream using handcrafted features and patient metadata. Texture and meta-level features are combined using dimension reduction and normalisation techniques with deep features. A weighted voting ensemble framework improves prediction stability. The presented system is intended to serve as a reliable, explainable, and precise means for clinical dermatology diagnosis.