Glaucoma Disease Classification by using Fundus images

Allah Rakha, Dr. Sohail Masood, Arslan Akram and Muhammad Faseeh Sultan
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Allah Rakha: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan
Dr. Sohail Masood: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan
Arslan Akram: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan, MLC Lab, Okara, 56310, Punjab, Pakistan
Muhammad Faseeh Sultan: Faculty of Computing, Department of Computer Science and Information Technology, The Superior University, Lahore, 54000, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 1, 736-748

Abstract: Optic Disc (OD) and Optic Cup (OC) damage is caused by the eye condition glaucoma. OD is the morphological structure that is apparent in the cross-sectional view of the optic nerve connecting to the retina, while OC is the core region of OD. The morphological changes in the optic disc (OD) and optic cup (OC) often happen before visual field issues when glaucoma begins. Optic nerve head damage caused by glaucoma is permanent. Glaucoma is the greatest global cause of irreversible blindness, according to data from the World Health Organization (WHO). Only 10 to 50 percent of glaucoma patients, according to population-level surveys, are aware that they have the condition. As a result, glaucoma early identification is crucial for preventing irreversible eye damage. Glaucoma is a vision disorder that frequently affects older people and renders them permanently blind. Glaucoma affects 2.5% of people of all ages and 4.8% of people over the age of 75. Using MobileNetV2, this study suggests a unique deep transfer learning model for categorizing glaucoma. With regard to the error, with the least amount of expense, MobileNetV2 is a framework that optimizes memory consumption and execution speed. To increase the dataset and MobileNetV2's precision, data augmentation techniques were used. Using the HRF dataset, the suggested deep learning model's effectiveness is assessed. Results from the suggested procedure are accurate to 98%. Medical professionals can find the optimum course of treatment for their patients with the help of automated glaucoma classification.

Keywords: Glacoma Disease Detection; Glaucoma disease classification; Glaucoma disease (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:1:p:736-748

DOI: 10.61506/01.00263

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