Elevating Skin Cancer Diagnosis: A Study of Various Machine Learning Approaches

The paper introduces a highly competitive multi-branch hybrid deep learning network integrating pre-trained EfficientNetB3, DenseNet121, and a customized Multi-Head Attention module to capture complex spatial linkages in dermoscopic skin lesions. By merging multi-scale textural features and global contextual correlation maps, the system successfully circumvents subjective diagnostic biases, capturing complex visual details to deliver a balanced 92.6% overall accuracy on the HAM10000 dataset for binary screening.