SKIN DISEASE CLASSIFICATION USING EFFICIENT TRANSFER LEARNING AND ATTENTION MECHANISM

Skin diseases are a common health issue that is often underestimated, as most are mild and can be treated with over-the-counter medications. However, some types, such as melanoma, can be cancerous and deadly if not treated properly. Melanoma is caused by excessive exposure to ultraviolet rays and has a recovery rate of 99% if diagnosed on time, but it decreases to 20% in advanced stages. This study developed a multi-category skin disease classification model using transfer learning through a previously trained model such as EfficientNetV2S with Attention Mechanism to overcome overfitting and improve accuracy. The dataset used is ISIC2019 with 8 classes of skin diseases and 25,331 samples, after data augmentation was performed to increase the sample size. The EffCANet model showed a test accuracy of 94.81%, higher than previous studies, indicating a decrease in the overfitting gap and an improvement in test accuracy results.