Simulation parameters.

Skin disease classification is a choir cognate for early diagnosis and therapy. The novelty of this study lies in integrating the Grasshopper Optimisation Algorithm (GOA) with a DETR (DEtection TRansformer) model which is developed for the classification of skin disease. Hyperparameter tuning using GOA optimizes the critical parameters of the proposed model to improve classification accuracy. After extensive testing on a large dataset of skin disease photos, the optimised DETR model returned an accuracy of at least 99.26%. The superiority of the DETR improved using GOA compared to standard ones indicates its potential to be used for automatically diagnosing skin diseases. Findings demonstrate that the proposed method contributes to enhancing diagnostic accuracy and creates a basis for improving transformer-based medical image analysis.