Authors:
Amir Hossein Panahi
1
;
Reza Askari Moghadam
1
and
Kurosh Madani
2
Affiliations:
1
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
;
2
LISSI Lab, Senart-FB Institute of Technology, University Paris Est-Creteil (UPEC), Lieusaint, France
Keyword(s):
Deep Learning, Residual-like CNN, Computer Vision, Image Segmentation, Glaucoma Detection, Eye Fundus, Optic Disc Segmentation, Medical Application.
Abstract:
Eye diseases such as glaucoma, if undiagnosed in time, can have irreversible detrimental effects, which can lead to blindness. Early detection of this disease by screening programs and subsequent treatment can prevent blindness. Deep learning architectures have many applications in medicine, especially in medical image processing, that provides intelligent tools for the prevention and treatment of diseases. Optic disk segmentation is one of the ways to diagnose eye disease. This paper presents a new approach based on deep learning, which is accurate and fast in optic disc segmentation. By Comparison proposed method with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, the proposed algorithm is much faster, which can segment the optic disc in 0.008 second with outstanding performance concerning IOU and DICE scores. Therefore, this method can be used in ophthalmology clinics to segment the optic disc in retina images and videos as online medical assistive
tool.
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