Abstract
Glaucoma is a fatal eye disease that harms the optic disc (OD) and optic cup (OC) and results into blindness in progressed phases. Because of slow progress, the disease exhibits a small number of symptoms in the initial stages, therefore causing the disease identification to be a complicated task. So, a fully automatic framework is mandatory, which can support the screening process and increase the chances of disease detection in the early stages. In this paper, we deal with the localization and segmentation of the OD and OC for glaucoma detection from blur retinal images. We have presented a novel method that is Densenet-77-based Mask-RCNN to overcome the challenges of the glaucoma detection. Initially, we have performed the data augmentation step together with adding blurriness in samples to increase the diversity of data. Then, we have generated the annotations from ground-truth (GT) images. After that, the Densenet-77 framework is employed at the feature extraction level of Mask-RCNN to compute the deep key points. Finally, the calculated features are used to localize and segment the OD and OC by the custom Mask-RCNN model. For performance evaluation, we have used the ORIGA dataset that is publicly available. Furthermore, we have performed cross-dataset validation on the HRF database to show the robustness of the presented framework. The presented framework has achieved an average precision, recall, F-measure, and IOU as 0.965, 0.963, 0.97, and 0.972, respectively. The proposed method achieved remarkable performance in terms of both efficiency and effectiveness as compared to the latest techniques under the presence of blurring, noise, and light variations.
Highlights
Glaucoma harms the optic nerve (ON) because of the imbalance of intraocular pressure within the eye. e affected nerve fibers result in deterioration of the retinal layer and give rise to the enlarged optic disc (OD), that is, the part of the retina, and the optic cup (OC) is the main portion of the OD
Glaucoma is typically analysed by attaining the medical history of patients, determining intraocular pressure (IOP), conducting visual field loss tests, and manual assessment of OD employing ophthalmoscopy to investigate the shape and color of the ON [1]. e cup-to-disc ratio (CDR) is one of the key structural image cues reflected for glaucoma identification. e CDR compares the diameter of OC with the diameter of OD; less than 0.5 CDR considers the normal value [2]
We initialized the model using pretrained weights obtained from the COCO dataset and employed transfer learning to fine-tune the model on retinal datasets for OD and OC segmentation
Summary
Glaucoma harms the optic nerve (ON) because of the imbalance of intraocular pressure within the eye. e affected nerve fibers result in deterioration of the retinal layer and give rise to the enlarged OD, that is, the part of the retina, and the OC is the main portion of the OD. Glaucoma harms the optic nerve (ON) because of the imbalance of intraocular pressure within the eye. Glaucoma is typically analysed by attaining the medical history of patients, determining intraocular pressure (IOP), conducting visual field loss tests, and manual assessment of OD employing ophthalmoscopy to investigate the shape and color of the ON [1]. Timely detection of disease can avoid blindness [3]. Experts identify eye abnormalities through the manual examination of the glaucoma regions, by calculating the CDR, diameter, and boundaries variations [5]. Due to the lack of available experts, timely identification of the eye abnormality is typically delayed [6], whereas early detection and treatment of the disease can save the victim from complete blindness. To tackle with mentioned challenges, the research community is targeting disease identification via ComputerAided Diagnosis (CAD) based solutions
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