A multi-modal deep learning framework for skin lesion classification on the HAM10000 dataset, combining dermoscopic images and clinical metadata using a ConvNeXt-Tiny backbone to achieve robust performance under class imbalance.

A multi-modal deep learning framework for skin lesion classification that combines dermoscopic images with clinical metadata using an EfficientNet-based CNN and an MLP branch. Evaluated on the HAM10000 dataset with class imbalance handling.

A high-resolution (384×384) multi-modal deep learning framework for skin lesion classification on the HAM10000 dataset, combining dermoscopic images and clinical metadata using EfficientNetB1 and a two-stage fine-tuning strategy to improve minority-class performance.