Assessing Pretrained Model Through Transfer Multi-Task Learn For Melanoma Classification

Melanoma is a lethal skin cancer that is increasingly threatening the public health system due to increased incidence rates and mortality rates. Early detection of the disease is vital for improved outcomes and the reduction of mortality rates. Skin cancer classification remains a challenging task in the field of dermatology. While self-attention mechanisms and large language models have gained traction in skin cancer detection research, there is still insufficient evidence demonstrating their superior performance compared to CNNs. Thus, further exploration of this area is warranted. Where the quest for the optimal CNN pretrained model persists. In this study, we address this gap by assessing various pretrained models to determine the most effective one for skin cancer classification. Additionally, we introduce a novel approach that leverages transfer learning to develop a multi-task model capable of providing more comprehensive prediction information from dermatological images. Unlike conventional single output classification tasks that rely solely on label prediction, our proposed model utilizes transfer learning techniques to extract valuable features from pretrained models, enhancing its ability to predict multiple tasks simultaneously. This novel approach not only advances the field of dermatology by improving classification accuracy but also meets the growing demand for more informative predictions in clinical settings.