ItemConference Paper

Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection

Title
Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection
Creator
Manju, V.N.; Dayana, D.S.; Patwari, Neha; Bindu Madavi, K.P.; Krishna Sowjanya, K.
Description
Skin cancer is one of the most prevalent and potentially fatal diseases, requiring early and accurate detection for effective treatment. Recent advances in deep learning have significantly improved automated skin lesion classification, but traditional Convolutional Neural Networks (CNNs) struggle with capturing long-range dependencies in dermoscopic images. To address this limitation, we propose a Preprocessing-Optimized Vision Transformer (ViT) Model that enhances lesion detection using attention-based feature fusion. Our methodology includes contrast enhancement (CLAHE), hair removal (DullRazor), lesion segmentation (K-Means + Otsus Thresholding), and data augmentation, ensuring robust model training. The proposed Attention-Enhanced ViT Model effectively learns global contextual features from dermoscopic images through self-attention mechanisms. The proposed model is evaluated our model on the ISIC Skin Cancer Dataset, achieving an accuracy of 94.6%, precision of 92.8%, recall of 93.5%, and an AUC-ROC score of 0.97, outperforming traditional CNN-based models such as ResNet50 (92.1% accuracy) and EfficientNet-B0 (93.3% accuracy). Our results demonstrate that integrating preprocessing techniques with Vision Transformers significantly enhances classification performance, making this approach a viable solution for real-world computer-aided dermatology. 2025 IEEE.
Source
Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Attention Mechanism; Contrast Enhancement (CLAHE); Dermoscopic Image Analysis; Feature Fusion; Lesion Segmentation; Skin Cancer Detection; Vision Transformer (ViT)
Coverage
Manju V.N., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India; Dayana D.S., Dept. of Networking and Communications, SRM Institute of Science and Technology, Chengalpet District, Kattankulathur, India; Patwari N., Dept. of Information Technonology, Thakur College of Engineering & Tech., Mumbai, India; Bindu Madavi K.P., Dept. of Computer Science and Engineering, Christ University, Bengaluru, India; Krishna Sowjanya K., Dept. of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152476-0;
Format
online
Language
English
Type
Conference paper

Citation

Manju, V.N.; Dayana, D.S.; Patwari, Neha; Bindu Madavi, K.P.; Krishna Sowjanya, K., “Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25832.

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