Exploring Artificial Intelligence Techniques for Diabetic Retinopathy Detection

Author:

Vijayalakshmi A.1,Unnisa Sarwath2,Joshi Hemlata1

Affiliation:

1. Christ University, India

2. Mount Carmel College, India

Abstract

There is a notable increase in the prevalence of Diabetic Retinopathy (DR) globally. This increase is caused due to type2 diabetes, diabetes mellitus (DM). Among people, diabetes leads to vision loss or Diabetic Retinopathy. Early detection is very much necessary for timely intervention and appropriate treatment on vision loss among diabetic patients. This chapter explores how Artificial Intelligence (AI) methods are helpful in automated detection of diabetic retinopathy. In this chapter deep learning algorithm is proposed that is used to extract important features from retinal images and classify the images to identify the presence of DR. The model is evaluated using various metrics like specificity, sensitivity etc. The results of the case study provide an AI driven solution to existing methods used to identify DR and this can improve the early detection and appropriate treatment at the right time.

Publisher

IGI Global

Reference17 articles.

1. Understanding of a convolutional neural network

2. Diabetic retinopathy detection through deep learning techniques: A review

3. BhardwajA.DiW.WeiJ. (2018). Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling. Packt Publishing Ltd.

4. Diabetic Retinopathy Detection Using Machine Learning and Texture Features

5. Diagnosis of Diabetic Retinopathy Using Deep Neural Networks

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