Diabetic Retinal Anomaly Prediction Using Keras Model Of Neural Networks

Registration ID: IJNRD_195055

Published ID: IJNRD2305375

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Rohan S, Rekha Lavanya, Akshay S Katti, Srinivas B V

Diabetes mellitus, Convolution Neural Networks (CNN), data augmentations, Image Classification, Diabetic Retinopathy(DR).

Diabetic retinopathy, a complication of diabetes mellitus caused by elevated blood sugar levels, can cause harm to the retina located at the back of the eye. Failure to detect and treat this condition appropriately may result in loss of eye sight. Electrical signals are being formed from the light rays with the help of the retina located behind the eye it contains Light-sensitive radiation. The brain receives the signals and converts them into the visuals you see. Continual blood flow is necessary for the retina, which is provided through a system of tiny blood capillariesIn severe cases of diabetic retinopathy, surgical removal and replacement of the vitreous, a substance which is gel like located behind the eye, may be necessary. Additionally, surgery may be required for a retinal detachment. Rear separation is being done here. We offer a CNN method for accurately determining the degree of DR from digital fundus pictures. [1-4] Through the use of CNN architecture and data augmentations, we have created a network that has the ability to identify the intricate components of the classification task, such as Tiny dilation or outpouchings of small blood vessels, Abnormal fluid or protein accumulation in tissues or body cavities, and Bleeding within the layers of the retina, the light-sensitive tissue located at the back of the eye. This network can provide automatic diagnosis without any input from the user. The training data consisted of images that had undergone Gaussian filters. Our research demonstrates the accuracy of the proposed CNN was recorded at 98%, while the sensitivity was over 95% on a set of 3500 validation photos. The implementation of this method removes the necessity for a retina specialist and enhances accessibility to retinal treatment, while offering a reliable and objective diagnosis and grading of diabetic retinopathy. Early disease detection and objective disease progression tracking are made possible by this method, which may help the improvement of medical treatment to lessen vision loss.

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Rohan S, Rekha Lavanya, Akshay S Katti, & Srinivas B V (May-2023). Diabetic Retinal Anomaly Prediction Using Keras Model Of Neural Networks. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), d597-d604. https://ijnrd.org/papers/IJNRD2305375.pdf

Volume 8 Issue 5, May-2023

Pages : d597-d604

Paper Reg. ID: IJNRD_195055

Published Paper Id: IJNRD2305375

Research Area: Information Technology 

Author Type: Indian Author

Country: Bengaluru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305375.pdf

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