Processing Retinal Images to Discover Diseases

Kazi, Ayesha; Ajmera, Miloy; Sukhija, Prerna; Devadkar, Kailas

doi:10.1109/icctct.2018.8550942

Cited by 4 publications

(1 citation statement)

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“…CNN is categorized as a Deep Neural Network due to its high network depth and widespread application to image data. Kunihiko Fukushima, a researcher at the NHK Broadcasting Science Research Laboratories in Kinuta, Setagaya, Tokyo, Japan, created CNN under the name NeoCognitron [15]. The proposed CNN Model's architecture is depicted in Figure 3.…”

Section: Fig 2 Sample Of Intensity Value Of Rgb C Architecture Of Cnnmentioning

confidence: 99%

Early Detection of Eye Disease Using CNN

Erdin¹,

Patel²

2023

IJRASET

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The eyes perform an essential part of life. The eye is one of the organs that assist humans learn about their natural environments and collect information from them. Almost everywhere in the world, the frequency of eye disease has increased, needing a serious response. Immediate eye detection will be invaluable assistance in offering further treatment to prevent blindness. In this study, a Convolution Neural Network model is used for identifying eye diseases. This research aims to categorize human eyes into four categories: trachoma, conjunctivitis, cataract, and healthy. The investigation got an accuracy of 88.36%, while the CNN model evaluation provided Precision of 89.25%, Recall of 88.75%, and F1 Score of 88.5%. Based on the accuracy and evaluation results, this system can be used for the early detection of multiple eye diseases. Several random samples were also used for testing in this investigation. The test results indicate that this system is functional.

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Section: Fig 2 Sample Of Intensity Value Of Rgb C Architecture Of Cnnmentioning

confidence: 99%