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- W4296630058 abstract "The further proliferation of age-related eye diseases, mainly age-related macular degeneration (ARMD), is increasing the load on healthcare providers. Although ARMD does not lead to complete blindness, the disease can make it difficult for people to perform daily activities such as driving, reading, writing, cooking, etc. The unavailability of any cure for ARMD, necessitates timely actions of detecting the first symptoms of eye conditions as well as following appropriate treatment options to minimize further damage. Some of the current techniques used to detect and monitor ARMD include the Amsler’s Grid, Near Vision Chart, Optical Coherence Tomography (OCT), etc. which are generally performed on paper in hospitals or clinics. This proposed solution facilitates prediction of age-related macular degeneration in patients using data collected through a Mobile application. The proposed system includes the digitization of paper-based tests as well as a novel approach for prediction of ARMD through Deep Learning. The system eliminates the need to visit a clinic and can be used by citizens from home at their discretion. The high prediction accuracy obtained while real-time testing and prediction of ARMD validates the effectiveness of the proposed approach." @default.
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- W4296630058 date "2022-09-23" @default.
- W4296630058 modified "2023-09-25" @default.
- W4296630058 title "Prediction of Age-Related Macular Degeneration (ARMD) Using Deep Learning" @default.
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- W4296630058 doi "https://doi.org/10.1007/978-981-19-2535-1_40" @default.
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