E-TLCNN Classification using DenseNet on Various Features of Hypertensive Retinopathy (HR) for Predicting the Accuracy

Hypertensive retinopathy (HR)is one of the severe damages caused in retinal vascular due to the hectic and abnormal upset in a human life. These damages are taken in a clinical sample by monitoring the presence and importance of all parameters that shows the increasing rate. Retina as main features for finding the variation which takes a longer duration to fix the diseases and impact in arteriolar construction and optic disk edema. In this work, a enhanced transfer learning-convolutional neural network (E-TLCNN) model is proposed for diagnosing HR using high quality images from fundus images. Therefore for understanding the results in an accurate manner transfer learning is used for classifying its stages. Also a new model using CNN architecture as DenseNet has been proposed for classifying the features to focus on the severity such as reading the diabetic retinopathy and AVR that can be utilized to spot the diseases. Dataset from Kaggle shows a 96% of accuracy in classifying the sensitivity along with its training and testing data whereas the comparison of image classification fetched less compared with K-nearest neighbor algorithm (KNN). Also based on the accuracy achieved validation process also done using the remaining images from the dataset.