Diabetic Retinopathy Detection using Machine Learning (Image Processing): A Review

Tai U. Choramale; Dr. Sharmila M. Shinde

Diabetic Retinopathy Detection using Machine Learning (Image Processing): A Review

Authors: Tai U. Choramale, Dr. Sharmila M. Shinde

Unique Paper ID: 175580
Volume: 11
Issue: 11
PageNo: 3728-3733

Keywords: Diabetic retinopathy (DR) Convolutional Neural Networks (CNNs) CNN-based Detection System Deep Learning etc.

Abstract:
Diabetic retinopathy (DR) is a serious complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and intervention are crucial in preventing vision loss among diabetic patients. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in various medical image analysis tasks, including the detection of diabetic retinopathy. This paper presents a novel approach for the automated detection of diabetic retinopathy using CNN’s. Our proposed method involves preprocessing retinal fundus images to enhance contrast and remove noise, followed by feature extraction using a pre-trained CNN architecture. The extracted features are then fed into a classification model for the detection of diabetic retinopathy. We utilize a large dataset of annotated retinal images to train and validate our CNN-based detection system, ensuring robust performance across diverse clinical scenarios. Experimental results demonstrate the effectiveness of our approach in accurately detecting diabetic retinopathy, achieving state-of-the-art performance in terms of sensitivity, specificity, and overall accuracy. Moreover, the proposed method exhibits robustness to variations in image quality and pathological characteristics, making it suitable for real-world clinical applications. In conclusion, our study highlights the potential of deep learning, specifically CNNs, as a valuable tool for the early detection and management of diabetic retinopathy. The proposed framework holds promise for integration into existing healthcare systems, facilitating timely diagnosis and intervention to prevent vision loss among diabetic patients.

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 3728-3733

Diabetic Retinopathy Detection using Machine Learning (Image Processing): A Review

Available:https://ijirt.org/Article?manuscript=175580

19 Apr, 2025

Plant Disease Recognition From Fruit Using CNN

16 Apr, 2025

Advanced Image-Based Malware Detection Leveraging Sparse CNN Networks for Intelligent Feature Recognition and Classification: A Review

03 Apr, 2025

Plant Disease Recognition From Fruit Using CNN

01 May, 2024

RFID Based Smart Shopping Kart using IOT

20 Apr, 2024

Solar Wireless Electric Vehicle Charging System

16 Apr, 2024

Solar Based Seed Sprayer

15 Apr, 2024

Automatic Power Factor Corrector using Capacitor Bank

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry