Review Article
Volume 11 Issue 2 - 2020
Deep Learning Model for Detection of Retinal Vessels from Digital Fundus Images- A Survey
Md Mohaimenul Islam Hsuan1,2, Tahmina Nasrin Poly1,2, Suleman Atique4, Yu-Chuan (Jack) Li1,2,3 and Hsuan Chia Yang1,2*
1Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
2International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan
3Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan
4Department of Health Informatics, College of Public Health and Health Informatics, University of Hail, Saudi Arabia
*Corresponding Author: Hsuan Chia Yang, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Received: October 24, 2019; Published: January 17, 2020


Computer-aided detection (CAD) system is a realistic option for physicians to screen fundus images. Automated segmentation of retinal vessel is in fundus important step to identify the retinal disease region. However, identification of the retinal disease region accurately is still challenging due to the varied distribution of blood vessel on noisy and low contrast fundus images. Healthcare system has been changing significantly with the emergence of machine learning (ML), deep learning (DL) and artificial intelligence (AI) in recent year. Retinal vessel detection is one such area of application of deep learning, for improving the accuracy of detection and segmentation and the quality of patient care. Recently, the convolutional neural networks (CNN) have been applied to the detection of the retinal vessel from fundus images and have demonstrated promising results. The range of accuracy of the CNN model was 0.91 - 0.95 and the area under the receiver operating curve was 0.09 - 0.98. Therefore, CNN may play a crucial role in determining the therapeutic methods and detecting the retinal vessel accurately in an individual manner. In this survey, we described the use of CNN in fundus imaging, especially focused on CNN technique, clinical application for retinal vessel detection and future prospective.

Keywords: Computer-Aided Detection; Retinal Vessels; Convolutional Neural Network; Image Detection


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Citation: Hsuan Chia Yang., et al. “Deep Learning Model for Detection of Retinal Vessels from Digital Fundus Images- A Survey”. EC Ophthalmology 11.2 (2020): 01-10.

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