Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Detecting Lyme Disease using YOLO Algorithm

Authors
M. Kiruthiga Devi1, *, V. Theerthan1, S. B. Sharath Ragava Krishnan1, S. Mahenthiravarman1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, India
*Corresponding author. Email: kiruthim6@srmist.edu.in
Corresponding Author
M. Kiruthiga Devi
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_28How to use a DOI?
Keywords
Lyme disease; YOLO algorithm; deep learning; object detection; AI in medical imaging; early diagnosis
Abstract

Tick-borne Borrelia burgdorferi caused Lyme di1sease remains a major public health concern due to its potential to cause serious complications if left untreated. Traditional diagnosis methods such as ELISA and Western Blot are hindered by late reports and false negative results, particularly in the situation of early infection, leading to misdiagnosis and treatment failure. This research responds to these issues by employing the You Only Look Once (YOLO) algorithm, deep learning object detection model, to identify Lyme disease indicators from clinical images in real-time. The study entails training the YOLO model using dataset of Lyme disease-related medical images with preprocessing techniques like image augmentation and normalization used to improve accuracy. The proposed answer enables faster and more precise identification, reducing reliance on conventional testing and providing an automated, scalable diagnostic tool. The results show that pairing YOLO with medical imaging enhances diagnostic performance, resulting in AI-assisted disease detection and health care innovation.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_28How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Kiruthiga Devi
AU  - V. Theerthan
AU  - S. B. Sharath Ragava Krishnan
AU  - S. Mahenthiravarman
PY  - 2025
DA  - 2025/10/31
TI  - Detecting Lyme Disease using YOLO Algorithm
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 333
EP  - 344
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_28
DO  - 10.2991/978-94-6463-866-0_28
ID  - Devi2025
ER  -