Automatic Retinal Image Analysis for the Detection of Diabetic Retinopathy

Author:

Porwal Prasanna1,Pachade Samiksha1,Kokare Manesh1,Deshmukh Girish2,Sahasrabuddhe Vivek3

Affiliation:

1. Shri Guru Gobind Singhji Institute of Engineering and Technology, India

2. Dr. G. A. Deshmukh Eye Clinic, India

3. Shankarrao Chavan Government Medical College, India

Abstract

Diabetic Retinopathy, a condition in the person affected by diabetes, is most common cause of blindness in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. With proper self-care, management, and medical professional support, individuals with diabetes can live a healthy and long life.

Publisher

IGI Global

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Derin Öğrenme Kullanılarak Fundus Görüntülerinden Katarakt ve Diyabetik Retinopati Tespiti;Mühendislik Bilimleri ve Araştırmaları Dergisi;2023-10-27

2. GlorotUniform Kernel Initializer based Conv2D Sequential Convolutional Neural Network for Diabetic Retinopathy Detection;2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT);2023-10-20

3. Fundus Images Enhancement Using Gravitational Force and Lateral Inhibition Network for Blood Vessel Detection;Studies in Computational Intelligence;2021-12-16

4. Laser Scar Classification in Retinal Fundus Images Using Wavelet Transform and Local Variance;Computer Vision and Machine Intelligence in Medical Image Analysis;2019-08-29

5. A Novel Method to Detect Fovea from Color Fundus Images;Advances in Intelligent Systems and Computing;2018-09-13

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