- Title
- Advancing skin cancer classification across multiple scales with attention-weighted transformers
- Creator
- Yang, Guang; Luo, Suhuai; Li, Jiaming
- Relation
- Fourth Symposium on Pattern Recognition and Applications (SPRA2023). Proceedings of Fourth Symposium on Pattern Recognition and Applications (SPRA 2023), Volume 13162 (Napoli, Italy 01-03 December 2023) p. 1316205-1316205
- Publisher Link
- http://dx.doi.org/10.1117/12.3030006
- Publisher
- SPIE - International Society for Optical Engineering
- Resource Type
- conference paper
- Date
- 2024
- Description
- Skin cancer remains a pressing global health concern, with millions of cases diagnosed annually. Early detection is vital, as survival rates vary significantly depending on the stage of diagnosis. Recent advances in dermatological practice have embraced dermoscopy, but its effectiveness often relies on practitioner experience, leading to diagnostic inconsistencies. In the realm of skin cancer classification, traditional machine learning methods gave way to deep learning, with vision transformers gaining prominence. This paper introduces a novel approach that leverages attention-weighted transformers for skin tumor classification. Attention weights gauge the significance of image patches, enabling precise region attention. Within our proposed framework, we introduce an enhanced transformer structure that capitalizes on the power of self-attention mechanisms. This architecture acquires discriminative region attention across multiple scales, enabling the model to effectively capture intricate image details and patterns. Experimental validation compares our method against Inception ResNet with soft attention and ViT-Base on the HAM10000 dataset. Data preparation involves duplicate removal, class rebalancing, and pixel-level augmentation. Evaluation metrics encompass accuracy, precision, sensitivity, specificity, and the F1 score. Results show our approach outperforms existing methods, achieving an accuracy of 93.75%. This work represents a significant stride toward accurate skin tumor classification, marrying innovative architecture with meticulous dataset preparation. The proposed approach holds potential to advance diagnostic tools for skin cancer, benefiting medical practitioners and patients alike.
- Subject
- transformers; skin cancer; image processing; performance modeling; visual process modeling; data modeling; SDG 3
- Identifier
- http://hdl.handle.net/1959.13/1515716
- Identifier
- uon:56910
- Identifier
- ISSN:0277-786X
- Language
- eng
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