DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING

Abstract

Diabetic Retinopathy (DR) is a very critical eye disease that is a cause of blindness and vision problems globally and is one of the most serious diabetes-related eye diseases. It results from the damage to blood vessels in the retina from diabetes. The early diabetic retinopathy may not have obvious symptoms and diagnosis is very critical to prevent permanent loss of vision. A conventional diagnosis of DR is currently based on manually analysis of retinal images, conducted by an ophthalmologist, but this process is expensive, time taking and often impractical, especially in rural and resource-constrained healthcare settings where healthcare specialists are not readily available. Hence, the need of intelligent and automated diagnostic system for early detection and accessibility to healthcare system is of utmost necessity.This research introduces theEye-RDNN: a deep learning framework designed for the classification of diabetic retinopathy in fundus imagesof retina with several classes. The suggested model features convolutional neural network, along with transfer learning, to automatically classify various stages of DR in retinal images. During the training process, image resizing, normalization, sample augmentation (rotation, zooming, brightness enhancement, and flipping), and other techniques were applied to the image set to improve the performance of the model. These procedures have enhanced the diversity of datasets, reduced overfitting, and boosted the feature extraction capacity of the models.The entire model was developed on TensorFlow/Keras models using Google Colab with help of support of GPUs for efficient computing and faster trainings. The choice of the base architecture was EfficientNet-B0 due to its efficient computations and excellent feature extraction property which proved to be adequate for the purpose of medical image classification. The accuracy of the trained model in classification of multiple stages of DR was highly satisfactory for images of the healthy retina as well as 6 severitylevels of diabetic retinopathy.The system designed can help the ophthalmologist carrying out the retinal screening faster and efficient, which will reduce the manual workload and the risk of delayed diagnosis. This sort of automated systems are especially helpful in far-off health care areas the place skilled ophthalmologists are scarce. Furthermore, the proposed approach could potentially be applied in large scale DR screening initiatives and enhance the detection of early DR. Additional features for the model could be employed to get even the better results, such as increase in the count of patients included in the data set analyzed and including diversity and varieties in the data, using more sophisticated and complex deep learning algorithms and booking the model for clinical validation, which could lead us to a higher rate of correct and accurate diagnosis and an increase of clinical practices that could be applied from the model.