Author(s) :
G. Kharmega Sundararaj
Article Name :
Enhanced Prediction of Diabetic Retinopathy Types Using Deep Convolutional Neural Networks
Abstract :
Diabetic retinopathy is an eye condition which leads to the loss of eye sight in the person who already has diabetics. There are several stages in detecting the diabetic retinopathy which includes manual medical methods or detected by automatic methods using image processing. In these cases, neural networks are used in detecting the defects caused in the retina of the diabetics. These neural network process as same as human brain, in this methodology Artificial Intelligence (AI) is implemented. In this methodology, the image is pre processed to improve the image clarity by Gaussian filtering method. The pre processed image undergoes segmentation using the Fuzzy C means (FCM) clustering method. By involving these methods, the complexity in detecting the diabetic retinopathy is reduced. The Deep Convolutional Neural Network (DCNN) method is implemented in which data is compared to find the defect. The pooling layer samples the image to speed up computation, and the fully connected layer provides the final prediction. In the Convolutional Neural Network, the data to the compared is already processed and stored. This paper provides excellent specificity and sensitivity for classifying images as having or without diabetic retinopathy. This paper is implemented using Python Google Colab software.
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