Journal of Engineering, Project, and Production Management, 2027, 17(2), 2026-209
A Novel Skin Disease Identification Algorithm Based on Attention Mechanism and Transformer Architecture
1 Ph.D. Candidate,
Associate Professor and Vice Dean, School of Artificial Intelligence and
Data Science, Jiangxi Polytechnic Institute, Xinyu, 338000, China,
E-mail: qinlanwu@outlook.com (corresponding author).
2 Associate Professor, School of Artificial Intelligence and
Data Science, Jiangxi Polytechnic Institute, Xinyu, 338000, China,
E-mail: hailinzou@heu.uu.me
3 Professor and Ph.D. candidate, School of Artificial
Intelligence and Data Science, Jiangxi Polytechnic Institute, Xinyu,
338000, China, E-mail: chaoxijian@heu.uu.me
Project Management
Received February 8, 2026; revised July 16, 2026; September 15, 2026; accepted October 14, 2026
Available online October 26, 2026
Abstract: Deep learning-based skin disease recognition technology has been continuously developing, but existing methods still fall short in terms of detail representation and computational efficiency due to limitations such as limited image samples, small lesion areas, and significant fine-grained differences. Against this backdrop, this study proposes a skin disease recognition algorithm based on an attention mechanism and a Transformer architecture. While retaining the local texture-feature modeling capability of convolutional networks, a Transformer recognition framework that integrates convolutional features with an attention mechanism is constructed to jointly model both local fine-grained features and global semantic information. Experimental validation was conducted using the International Society for Skin Imaging Collaboration (ISIC) 2018 dataset. The proposed method achieved good classification performance on this dataset, with a Macro-F1 score of 0.90 and a Weighted-F1 score of 0.95, and demonstrated superior recognition performance in comparisons with various Transformer models. The results show that this method can effectively integrate local fine-grained features and global semantic information, which is significant in improving the accuracy and stability of automatic skin disease recognition and provides a feasible technical path for intelligent assisted diagnosis of skin diseases.
Keywords: Skin disease identification; multi-scale local feature modeling; lesion perception attention mechanism; transformer architecture; medical image analysis.
Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.
Requests for reprints and permissions at eppm.journal@gmail.com.
Citation: Wu, Q., Zou, H., and Chao, X. (2027). A Novel Skin Disease Identification Algorithm Based on Attention Mechanism and Transformer Architecture. Journal of Engineering, Project, and Production Management, 17(2), 2026-209.
