HEVA: Integrating ViT and ResNet50 with ABCD Clinical Features for Skin Cancer Diagnosis

Melanoma is the deadliest skin cancer, causing over 90% of skin tumor deaths. Early detection ensures survival above 95%, but below 15% in late stages. We propose the HEVA architecture, which integrates ensemble deep learning with ABCD descriptors and a SegFormer-B5 model, for accurate lesion masks. HEVA achieved 93.2% accuracy and 84.2% F1-score on HAM-10000, and 87.7% accuracy with 82.01% macro F1 on ISIC 2018 (1,512 images). SegFormer achieved 82.6% mean IoU and 91.2% accuracy in segmentation.