A Hybrid IncepX-Ensemble Model for Multiclass Skin Lesion Detection and Classification

Abstract

Skin cancer is one of the most common cancers worldwide, and melanoma is especially dangerous if it is not de-tected early. Early diagnosis can improve survival rates. Dermo-scopic images are widely used by dermatologists to examine skin lesions. But manual analysis can be difficult and depends heavily on clinical experience. So automated methods for skin lesion classification have gained much attention. A hybrid deep learning model is proposed for multiclass skin lesion classification, aiming to improve diagnostic accuracy and robustness across diverse lesion types. The model combines two pretrained architectures, InceptionV3 and Xception, to extract features from dermoscopic images. The features obtained from both networks are fused to improve the overall representation. Transfer learning is applied to make use of pretrained weights, and data augmentation techniques are used to reduce overfitting and improve model stability. The proposed method is evaluated on a seven-class dermoscopic image dataset. The results show that the ensemble model performs better than the individual models. In addition, explainable AI techniques are used to highlight the important regions in the images that influence the model’s predictions, which improves interpretability and supports its practical use.