Aqthobirrobbany, Aqil and Hardani, Dian Nova Kusuma and Soesanti, Indah and Nugroho, Hanung Adi (2023) A systematic review of breast cancer detection on thermal images. Communications in Science and Technology, 8 (2). pp. 216-225.
1270-Article Text-3816-1-10-20231231 (1).pdf - Published Version
Restricted to Registered users only
Download (679kB) | Request a copy
Abstract
Breast cancer poses a substantial global health concern, primarily regarding its impact on women. Thermal imaging has emerged as a promising tool for early detection with notable technological advancements between 2013 and 2023 in enhancing diagnostic capabilities. However, existing literature reviews often lack adherence to specific scholarly standards and may provide incomplete insights into research trends. This systematic literature review (SLR) addresses these issues by comprehensively analyzing research trends, publication types, contributions, datasets, methodologies, and effective approaches for breast cancer detection using thermal imaging. The review encompasses an examination of 40 articles from reputable digital libraries, revealing a predominant emphasis on deep learning algorithms among 25 applied methods. These algorithms consistently achieve commendable performance, frequently surpassing 90% accuracy rates. Consequently, current research in breast cancer detection via thermal imaging is marked by a strong focus on artificial intelligence, particularly machine and deep learning, recognized as the most promising and effective avenues for investigation.
Item Type: | Article |
---|---|
Additional Information: | Library Dosen |
Uncontrolled Keywords: | Artificial intelligence; breast cancer; deep learning; systematic literature review; thermal breast images |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electrical and Information Technology Department |
Depositing User: | Rita Yulianti Yulianti |
Date Deposited: | 14 Aug 2024 03:02 |
Last Modified: | 14 Aug 2024 03:02 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/77 |