# Optimized Grasshopper Optimisation Algorithm enabled DETR (DEtection TRansformer) model for skin disease classification

## TL;DR

This paper introduces a new method for classifying skin diseases using an optimized DETR model enhanced by the Grasshopper Optimisation Algorithm, achieving high accuracy.

## Contribution

The integration of the Grasshopper Optimisation Algorithm with the DETR model for optimized skin disease classification is novel.

## Key findings

- The optimized DETR model achieved an accuracy of at least 99.26% on a large dataset of skin disease photos.
- The proposed method outperforms standard models, showing potential for automatic diagnosis of skin diseases.

## Abstract

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.