Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manju, V.N.; Dayana, D.S.; Patwari, Neha; Bindu Madavi, K.P.; Krishna Sowjanya, K. Title A name given to the resource Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ETCC65847.2025.11108453" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108453</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015616471?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015616471?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; 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 Description An account of the resource 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. Subject The topic of the resource Attention Mechanism; Contrast Enhancement (CLAHE); Dermoscopic Image Analysis; Feature Fusion; Lesion Segmentation; Skin Cancer Detection; Vision Transformer (ViT) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152476-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online