Burning Skin Detection System in Human Body

Abdulhadi, Noor M. and Ibraheem, Noor A. and Hasan, Mokhtar M. (2022) Burning Skin Detection System in Human Body. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 10 (2). pp. 169-178. ISSN 2410-9355

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Official URL: http://dx.doi.org/10.14500/aro.10976


Early accurate burn depth diagnosis is crucial for selecting the appropriate clinical intervention strategies and assessing burn patient prognosis quality. However, with limited diagnostic accuracy, the current burn depth diagnosis approach still primarily relies on the empirical subjective assessment of clinicians. With the quick development of artificial intelligence technology, integration of deep learning algorithms with image analysis technology can more accurately identify and evaluate the information in medical images. The objective of the work is to detect and classify burn area in medical images using an unsupervised deep learning algorithm. The main contribution is to developing computations using one of the deep learning algorithm. To demonstrate the effectiveness of the proposed framework, experiments are performed on the benchmark to evaluate system stability. The results indicate that, the proposed system is simple and suits real life applications. The system accuracy was 75%, when compared with some of the state-of-the-art techniques.

Item Type: Article
Uncontrolled Keywords: Skin burn, Clustering, Deep learning, Fuzzy c-means clustering, Image segmentation, Medical image
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: ARO-The Scientific Journal of Koya University > VOL 10, NO 2 (2022)
Depositing User: Dr Salah Ismaeel Yahya
Date Deposited: 03 Jan 2023 07:10
Last Modified: 03 Jan 2023 07:10
URI: http://eprints.koyauniversity.org/id/eprint/346

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