Efficient Detection of Diabetic Retinopathy through Deep Learning

P. Pushpa; P. Susmitha; V Archana; P. Amareswar Reddy; K. Bhanu Prakash

Efficient Detection of Diabetic Retinopathy through Deep Learning

Author(s)	P. Pushpa, P. Susmitha, V Archana, P. Amareswar Reddy, K. Bhanu Prakash
Country	India
Abstract	Diabetic retinopathy (DR), an incurable phenomenon in the retina (an irreversible disorder) caused by excessive levels of blood sugar, may potentially result in blindness. This study suggests a radical approach to automated DR detection method. Pre-processing of fundus images (FI) was done using the Contrast Limited Adaptive Histogram Equalization (CLAHE) to emphasize the lesions. After extracting features with the help of a convolutional neural network (CNN) we will classify DR with the help of the H5 model. The CNN design saves time required to elicit distinguishing features by eliminating less layers and parameters. And the results have a prospect. The other secondary result of the study, however, was that the proposed framework was stable in terms of balanced and imbalanced datasets and mega-sized and minimal datasets. Moreover, the suggested approach performed better than the state-of-the-art models to measure the level of classifier performance, model parameters and layers, and prediction time, which will significantly contribute to medical practitioners to appropriately interpret the DR.
Keywords	Convolutional neural network (CNN), Deep Learning, H-5 Model, Contrast Limited Adaptive Histogram Equalization (CLAHE), Diabetic Retinopathy
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-04-04
Cite This	Efficient Detection of Diabetic Retinopathy through Deep Learning - P. Pushpa, P. Susmitha, V Archana, P. Amareswar Reddy, K. Bhanu Prakash - IJAIDR Volume 17, Issue 1, January-June 2026.

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Efficient Detection of Diabetic Retinopathy through Deep Learning

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