TY  - EJOU
AU  - Ali, Armughan 
AU  - Shahbaz, Hooria 
AU  - Damaševičius, Robertas 

TI  - xCViT: Improved Vision Transformer Network with Fusion of CNN and Xception for Skin Disease Recognition with Explainable AI
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 1
SN  - 1546-2226

AB  - Skin cancer is the most prevalent cancer globally, primarily due to extensive exposure to Ultraviolet (UV) radiation. Early identification of skin cancer enhances the likelihood of effective treatment, as delays may lead to severe tumor advancement. This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis, with an architecture that integrates a Vision Transformer, a bespoke convolutional neural network (CNN), and an Xception module. They were evaluated using two benchmark datasets, HAM10000 and Skin Cancer ISIC. On the HAM10000, the model achieves a precision of 95.46%, an accuracy of 96.74%, a recall of 96.27%, specificity of 96.00% and an F1-Score of 95.86%. It obtains an accuracy of 93.19%, a precision of 93.25%, a recall of 92.80%, a specificity of 92.89% and an F1-Score of 93.19% on the Skin Cancer ISIC dataset. The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions. In addition, the utilization of Explainable AI techniques, such as Grad-CAM visualizations, assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
KW  - Skin lesions; vision transformer; CNN; Xception; deep learning; network fusion; explainable AI; Grad-CAM; skin cancer detection

DO  - 10.32604/cmc.2025.059301