The L2-Effnet for Human Skin Disease Multiclass Classification - Kurnia Adi Cahyanto; Kusworo Adi; Catur Edi Widodo (Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 2026)
Skin diseases are common health problems that are 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 overexposure to ultraviolet light and has a 99% cure rate if diagnosed early, but decreases to 20% in advanced stages. In Indonesia, the uneven distribution of medical personnel and geographical challenges cause many cases of skin diseases not to be diagnosed well. This study develops a multi-class skin disease classification model using transfer learning by utilizing pre-trained models such as EfficientNetV2S to overcome data imbalance and improve accuracy. The dataset used is a combination of ISIC and Atlas Dermatology, which has been curated into 31 classes with a total of 3,399 samples, and data augmentation was performed to increase the sample size. The modified L2-EfficientNetV2S model showed a testing accuracy of 88.33%, higher than previous studies, demonstrating the potential of using deep learning in the early diagnosis of skin diseases to improve healthcare.