Efficient Method for Detecting Abnormal Growth of Blood Vessels Using Convolutional Neural Network

Abstract

Diabetic Retinopathy is the main problem in human life because of high sugar levels present in the blood. Various organs get affected due to this reason. The eye is the one of the parts of the human eye which goes to vision problems sometimes blindness. The early stages of detection need to protect the human eye. Existing model uses various methods which do not solve the problem completely. Machine learning based approach introduced for detection of the affected area of eye. The Blood Vessels of the eye get affected as a result bleeding in the eye and excess growth in the eye. Traditional algorithms are not suitable for detecting this growth rate due to the less resolution images. The CNN based model with integrated data sets are used to classify and detect the blood vessel. The high resolution images are used for detecting the location and exact difference in normal vessels. Various algorithms are used with different data sets for making multidimensional analysis. The objective of this method is to identify the heterogeneous vessels from the normal vessel with a high level of accuracy.