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Cloud Based Ophthalmology Grader

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dc.contributor.author Project Supervisors Dr. Usman Akram Sir Ali Saeed, Khizar Hussain
dc.date.accessioned 2025-03-13T06:45:41Z
dc.date.available 2025-03-13T06:45:41Z
dc.date.issued 2021
dc.identifier.other DE-COMP-39
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/50975
dc.description Project Supervisors Dr. Usman Akram Sir Ali Saeed en_US
dc.description.abstract Ophthalmology is the branch of medical science that deals with treatment and surgery of the disorders that occur inside the eye. These disorders can be in the form of Diabetic Retinopathy, Glaucoma, AMD, Cataract etc and they constitute most of the cases of blindness which can be avoidable (80%). The combined total of the total number of people who are visually impaired people is 285 million and the number is expected to increase soon if no proper measures are taken. In Pakistan alone we have 480 people who daily develop some sort of eye disease so a cloud-based solution can work with 4G infrastructure in the country and provide access to any remote area. Also, the other problem is that in countries like Pakistan, a huge majority of graduates of Medicine are Females yet only 50% of them can practice after graduation due to social and cultural limitations. So a remote cloud based solution can help them practice ophthalmology while sitting at their homes. The diagnosis of ophthalmology disorders are done using two major techniques i.e OCT Scans and Fundus Image Examination. Previously, the whole methodology of treating and diagnosis of these disorders involved a cycle of multiple visits to the doctor back and forth. This project involved a solution which automates the long diagnosis cycle for these disorders and is beneficial for all stake holders – mainly focusing on a cloud based expandable system. The multiple modules which will be explained in the next part of the report can be used to replace the complete manual diagnosis by AI and Deep Learning based techniques. The doctors, ophthalmologists, graders, and adjudicators can login into the cloud-based platform and grade images from anywhere in the world. They are also able to provide their feedback which is used to perform Incremental Learning. The platform also gives complete reporting options for patients based on clinical findings. en_US
dc.language.iso en en_US
dc.publisher College of Electrical & Mechanical Engineering (CEME), NUST en_US
dc.title Cloud Based Ophthalmology Grader en_US
dc.type Project Report en_US


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