Version 1
: Received: 30 July 2024 / Approved: 30 July 2024 / Online: 31 July 2024 (02:19:01 CEST)
How to cite:
Chen, Y.; Liu, Z.; Meng, Y.; Li, J. Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network. Preprints2024, 2024072472. https://doi.org/10.20944/preprints202407.2472.v1
Chen, Y.; Liu, Z.; Meng, Y.; Li, J. Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network. Preprints 2024, 2024072472. https://doi.org/10.20944/preprints202407.2472.v1
Chen, Y.; Liu, Z.; Meng, Y.; Li, J. Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network. Preprints2024, 2024072472. https://doi.org/10.20944/preprints202407.2472.v1
APA Style
Chen, Y., Liu, Z., Meng, Y., & Li, J. (2024). Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network. Preprints. https://doi.org/10.20944/preprints202407.2472.v1
Chicago/Turabian Style
Chen, Y., Yujia Meng and Jianfeng Li. 2024 "Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network" Preprints. https://doi.org/10.20944/preprints202407.2472.v1
Abstract
Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.
Keywords
glaucoma screening; optic disc and optic cup segmentation; convolutional neural network; the adversarial generative network
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.
