10th International Scientific Conference Technics, Informatics and Education – TIE 2024, str. 83-88
АУТОР(И) / AUTHOR(S): Stefan Ćirković 
, Nikola Stanić
DOI: 10.46793/TIE24.083C
САЖЕТАК /ABSTRACT:
Skin cancer is one of the most common forms of cancer worldwide. Exposure to ultraviolet (UV) radiation increases the risk of its development. Early preventive examinations and early detection of suspicious skin changes are key factors for successful treatment. Due to the rapid development of AI technologies, neural networks have found application in various fields, including medicine. Neural networks can be used to create various applications, which would facilitate self-examination for patients and alert them to potential problems. This method would further save time and reduce healthcare costs. The paper presents the application of a neural network using the YOLO (You Only Look Once) algorithm on a dataset of mole images with the aim of identifying and classifying moles, which facilitates early intervention and improves treatment outcomes.
КЉУЧНЕ РЕЧИ / KEYWORDS:
skin cancer; yolo; neural network
PROJEKAT / ACKNOWLEDGEMENTS:
This study was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, and these results are parts of the Grant No. 451-03-66 / 2024-03 / 200132 with University of Kragujevac – Faculty of Technical Sciences Čačak.
ЛИТЕРАТУРА / REFERENCES:
- Arnold, M., Singh, D., Laversanne, M., Vignat, J., Vaccarella, S., Meheus, F., Cust, A. E., de Vries, E., Whiteman, D. C., Bray, F. (2022). Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatology, 158(5), 495-503. doi: 10.1001/jamadermatol.2022.0160
- Ünver, H. M., Ayan, E. (2019). Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm. Diagnostics, 9(3), Article 72. doi: 10.3390/diagnostics9030072
- Aggarwal, A., Das, N., Sreedevi, I. (2019). Attention-guided deep convolutional neural networks for skin cancer classification. 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), 1-6. doi: 1109/ipta.2019.8936100
- Ameri, A. (2020). A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images. Journal of Biomedical Physics and Engineering, 10(6), 801-806. doi: 10.31661/jbpe.v0i0.2004-1107
- Manoj, S. O., Abirami, K. R., Victor, A., Arya, M. (2023). Automatic Detection and Categorization of Skin Lesions for Early Diagnosis of Skin Cancer Using YOLO-v3 – DCNN Architecture. Image Analysis & Stereology, 42(2), 101-117. doi:10.5566/ias.2773
- (n.d.). Ultralytics Documentation. [Online]. Available: https://docs.ultralytics.com/. Accessed: 4 June 2024.
- Ragab, M. G., Abdulkadir, S. J., Muneer, A., Alqushaibi, A., Sumiea, E. H., Qureshi, R., Al-Selwi, S. M., Alhussian, H. (2024). A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023). IEEE Access. doi: 10.1109/access.2024.3386826