An automatic recognition of glaucoma in fundus images using deep learning and random forest classifier

Glaucoma is perpetual damage of optic nerves which causes fractional or complete visual misfortune. The fundamental reason for this illness is the increment of the intra-ocular pressure inside the eye which harms the optic nerve. Retinal images give indispensable data about an eye & rsquo;s wellbeing. Based on progressions in retinal images technology, it is conceivable to create frameworks that can investigate these retinal imageries for better determination. This work presents a glaucoma recognition technique by estimating CDR (Cup to Disc Ratio) from fundus images. The size of the optic disc and optic cup is utilized to distinguish the presence of glaucoma. Therefore, the segmentation of the optic disc and optic cup is the primary step in glaucoma recognition. Decreasing the number of features and reducing the error are the two clashing destinations. The proposed glaucoma recognition technique comprises image acquisition, feature extraction, and glaucoma evaluation stages. The contrast enhancement operation is performed in image acquisition. While boundaries of OD (Optic Disc) and OC (optic cup) are segmented in the feature extraction stage and it is performed by utilizing Au-Net. Then CDR ratio of an abused image is computed to assess glaucoma in the images. Thereafter, a random forest classifier has been used to classify the glaucomatous images based on the CDR values. The performance of the proposed method has been evaluated with different techniques such as Deformable U-Net, Full-Deformable U Net, and Original U-Net. From the outcomes, it can be noticed that the proposed method gives better performance in terms of classification accuracy of 99% and 14% of segmentation accuracy has been compared when compared with Original U-Net. (c) 2021 Elsevier B.V. All rights reserved. Superscript/Subscript AvailableMore