Usman, S.M., Shah, S.N.H., Dicheva, Nevena, Rehman, Ikram ORCID: https://orcid.org/0000-0003-0115-9024 and Zaib, S. (2024) Integrating Advanced Healthcare AI into Higher Education of Smart Cities: Skin Cancer Classification with Custom Vision Transformers. In: International Conference on Sustainability: Developments and Innovations, 18-22 Feb 2024, Riyadh, Saudi Arabia.

Full text not available from this repository.

Abstract

In today's smart cities, it's essential to combine advanced healthcare with education. Our research introduces a groundbreaking method for detecting skin cancer, using a new type of artificial intelligence called a Vision Transformer. This model analyzes a wide range of skin images from different people and accurately identifies skin cancer, especially melanoma and benign moles. Moving beyond traditional techniques like convolutional neural networks, our approach uses advanced attention mechanisms to improve accuracy by focusing on key areas in the skin images. The model has been thoroughly tested with specific criteria to ensure it can reliably detect skin cancer while reducing errors. This research is particularly important as skin cancer rates are increasing worldwide, and early, accurate detection is vital for saving lives. Our study focuses on providing assistance to healthcare practitioners for skin disease diagnostics as well as to assist in providing training and education to healthcare researchers. The proposed method outperforms in terms of ROC curve analysis.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	2366-2557
ISBN:	9789819783458
Identifier:	10.1007/978-981-97-8345-8_28
Identifier:	10.1007/978-981-97-8345-8_28
Subjects:	Computing > Intelligent systems
Related URLs:	https://link.springer.com/book/10.1007/9...
Depositing User:	Marc Forster
Date Deposited:	13 Jan 2025 09:16
Last Modified:	13 Jan 2025 09:22
URI:	https://repository.uwl.ac.uk/id/eprint/13082

Actions (login required)

View Item