Abstract
Because of the harmful effects of ultraviolet rays and global environmental factors, the number of patients with skin lesions is increasing. If left untreated, skin lesions may lead to skin cancer. However, limited access to specialized medical care remains a challenge in certain regions. Therefore, there is an urgent need for an efficient, accurate, and accessible tool to identify suspicious lesions. Although there are many classification models for skin lesions, there is still room for improvement in terms of accuracy. To enhance the accuracy of skin lesion classification, a novel system named SILP is proposed in this study. There are two modules in SILP: the LPM and the SIM. Additionally, we have changed the activation function from GELU to SiLU to improve both training time and accuracy. SILP, along with several other models, has been tested on two public skin lesion datasets. The results demonstrate that our proposed system outperforms the state-of-the-art skin lesion classification model, not only in terms of accuracy but also in various other evaluation metrics.
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