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8 April 2024 Advancements in Demodex mite detection: a comparative analysis of YOLOv5 and YOLOv8 utilizing microscopic examination images
Mini Han Wang, Xiaoxiao Fang, Zhiyuan Lin, Peijin Zeng, Yu Yang, Yunxiao Liu, Haoyang Liu, Wenhan Hu, Xinyue Li, Xudong Jiang, Guangshun Chen, Guanghui Hou, Kelvin KL Chong, Junbin Fang
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Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309016 (2024) https://doi.org/10.1117/12.3026178
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China
Abstract
This study conducts a rigorous comparative assessment of YOLOv5 and YOLOv8 for the detection of Demodex mites in microscopic examination images, leveraging crucial metrics such as accuracy, precision, recall, and F1-score. The investigation reveals the unequivocal superiority of YOLOv8, not only in quantitative measures but also substantiated by visual evidence, showcasing its applicability for real-time scenarios. YOLOv8 exhibits exceptional accuracy in overall detection and introduces a novel functionality for quantitative assessment of individual mites, providing essential granularity for precise diagnoses and therapeutic planning within dermatological and ophthalmological contexts. Positioned as a substantial advancement in object detection methodologies, YOLOv8 holds promise for significantly improving both accuracy and granularity in Demodex mite detection within microscopic examination images. While acknowledging potential limitations associated with dataset-specific considerations, this research underscores the imperative for further validation across diverse clinical scenarios. Computational considerations for real-time processing prompt future investigations to explore optimization strategies, particularly in resource-constrained environments. These findings position YOLOv8 as a valuable tool for clinicians and researchers engaged in dermatological and ophthalmological studies, offering heightened accuracy and nuanced insights. Ongoing research, encompassing clinical validations and comparative assessments with other state-of-the-art models, is anticipated to contribute to a more exhaustive understanding of YOLOv8’s potential and limitations in real-world applications based on microscopic examination images.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Mini Han Wang, Xiaoxiao Fang, Zhiyuan Lin, Peijin Zeng, Yu Yang, Yunxiao Liu, Haoyang Liu, Wenhan Hu, Xinyue Li, Xudong Jiang, Guangshun Chen, Guanghui Hou, Kelvin KL Chong, and Junbin Fang "Advancements in Demodex mite detection: a comparative analysis of YOLOv5 and YOLOv8 utilizing microscopic examination images", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309016 (8 April 2024); https://doi.org/10.1117/12.3026178
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KEYWORDS
Object detection
Artificial intelligence
Biomedical applications
Evolutionary algorithms
Accuracy assessment
Analytical research
Video
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