Version 1
: Received: 26 December 2023 / Approved: 26 December 2023 / Online: 27 December 2023 (05:16:40 CET)
How to cite:
Abay, S. G.; Geurts, L. Automated Optic Disc Localization from Smartphone-Captured Low Quality Fundus Images Using YOLOv8n Model. Preprints2023, 2023122042. https://doi.org/10.20944/preprints202312.2042.v1
Abay, S. G.; Geurts, L. Automated Optic Disc Localization from Smartphone-Captured Low Quality Fundus Images Using YOLOv8n Model. Preprints 2023, 2023122042. https://doi.org/10.20944/preprints202312.2042.v1
Abay, S. G.; Geurts, L. Automated Optic Disc Localization from Smartphone-Captured Low Quality Fundus Images Using YOLOv8n Model. Preprints2023, 2023122042. https://doi.org/10.20944/preprints202312.2042.v1
APA Style
Abay, S. G., & Geurts, L. (2023). Automated Optic Disc Localization from Smartphone-Captured Low Quality Fundus Images Using YOLOv8n Model. Preprints. https://doi.org/10.20944/preprints202312.2042.v1
Chicago/Turabian Style
Abay, S. G. and Luc Geurts. 2023 "Automated Optic Disc Localization from Smartphone-Captured Low Quality Fundus Images Using YOLOv8n Model" Preprints. https://doi.org/10.20944/preprints202312.2042.v1
Abstract
In areas where conventional fundus cameras are logistically unaffordable, the smartphone-based approach is considered as a promising future of glaucoma screening due to their affordability and ease-of-use. Optic disc localization is a critical stage during glaucoma screenings. For fundus images captured with standard fundus cameras, the majority of the models available out there can locate the optic disc with satisfactory performance. However, for images that are captured with smartphone-based fundus cameras, the inherent noise and lower quality of the fundus image makes it difficult for models to detect the optic disc region. In this study, we have proposed to utilize YOLOv8n for optic disc localization due to the model’s cutting-edge performance in diverse tasks and its lightweight nature. We have used a public dataset which has 2000 low quality fundus images that are captured using a smartphone-based fundoscopy device. From these, 60% of the data was used for fine-tuning the model, and 25% for testing. By using a confidence of 50% set as threshold, the model was able to detect the optic disc successfully on over 97% of test images with intersection over union of above 0.85. These results highlight the potential of the lightweight YOLOv8n model for deployment on resource-constrained environments, offering a promising performance on accurately localizing the OD and enhancing the feasibility of affordable glaucoma screening on smartphones.
Keywords
deep learning; glaucoma; localization; object detection; optic disc; transfer learning; YOLO; YOLOv8
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
