Deeply Dual Supervised learning for melanoma recognition

ArXi:2508.01994v2 Announce Type: replace As the application of deep learning in dermatology continues to grow, the recognition of melanoma has garnered significant attention, nstrating potential for improving diagnostic accuracy. Despite advancements in image classification techniques, existing models still face challenges in identifying subtle visual cues that differentiate melanoma from benign lesions. This paper presents a novel Deeply Dual Supervised Learning framework that integrates local and global feature extraction to enhance melanoma recognition.