Abstract: Not only can wounds impair a patient's physical and mental well-being, but they also result in significant medical expenses. In the meantime, there may be a physician scarcity in some locations, and clinical exams may not always be accurate in diagnosing wounds. Accurate wound analysis is crucial for its diagnosis, management, and care. Nowadays, machine learning has become the most widely used method for wound image interpretation due to its rapid development in the fields of computer vision and medical imaging. This work examines the state-of-the-art deep learning research on wound image processing, encompassing segmentation, detection, and classification. Firstly, we examine the pre-processing techniques utilized in wound image analysis and assess the publicly available datasets from different studies. Secondly, different models applied to diverse machine learning tasks (identification, classification, and segmentation) and their applications in different types of wounds (e.g., burns, cuts, lacerations) are investigated. In conclusion, we address the difficulties encountered using the field of machine learning wound image analysis and offer a future direction for research and growth.
Keywords: Wound diagnosis, Computer Vision, Detection, Segmentation.
Cite: KB Mangala, Umema Zaib, Divya M, Sarvar Begum, "Algorithms for determining the Injuries: A Survey", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13412.
|
DOI:
10.17148/IJARCCE.2024.13412
