TY - JOUR
T1 - Optimized Grasshopper Optimisation Algorithm enabled DETR (DEtection TRansformer) model for skin disease classification
AU - Kundu, Shakti
AU - Sharma, Yogesh Kumar
AU - Nabilal, Khan Vajid
AU - Samkumar, Gopalsamy Venkatesan
AU - Aldossary, Sultan Mesfer
AU - Rakesh, Shanu Kuttan
AU - Nuristani, Nasratullah
AU - Hashmi, Arshad
N1 - Publisher Copyright:
© 2025 Kundu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2025/5
Y1 - 2025/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=105007065078&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0323920
DO - 10.1371/journal.pone.0323920
M3 - Article
C2 - 40440246
AN - SCOPUS:105007065078
SN - 1932-6203
VL - 20
JO - PLoS ONE
JF - PLoS ONE
IS - 5 May
M1 - e0323920
ER -