ARCHIVES

Research Article

Ocular Disease Intelligent Recognition using Hybrid CNN Approach

Mohammed Ismail A1 Subash S2 Dhanush Kannan A3 Dr.J. B Jona4 S.A. Gunasekaran5
123 MSc (Integrated) Decision and Computing Sciences, Coimbatore Institute of Technology, Tamilnadu, India. 4Associate Professor, Department of Computer Applications, Coimbatore Institute of Technology, Tamilnadu, India. 5 Assistant Professor, Department of Computer Applications, Coimbatore Institute of Technology, Tamilnadu, India.

Published Online: May-June 2022

Pages: 469-472

Cite this article

No DOI

Abstract

Abstract: Ocular disease early detection is an economic and productive path to forestall visual defect caused by diabetes, glaucoma, cataract, age-related devolution (AMD), and plenty of other diseases. Currently according to planet Health Organization (WHO), a minimum of 2 billion people round the world have vision impairments, of whom a minimum of 1 billion have a vision impairment that might are prevented. Speedy and automatic detection of diseases is critical and urgent in reducing the ophthalmologist’s workload and prevents vision damage of patients. Computer vision and deep learning can automatically detect ocular diseases by providing high-quality medical eye fundus images.

Related Articles

2022

A Review on Bamboo Reinforced Concrete Beam

2022

FARMERS AGRICULTURAL PORTAL

2022

Sentiment Analysis of Religious Tweets

2022

Enhancement of beam strength by using bamboo as reinforcement in place of steel bars

2022

A Review on Anomaly Detection using PYOD Package

2022

Traffic Rule Violation Detection system

Share Article

X
LinkedIn
Facebook
WhatsApp

Or copy link

https://theijire.com/archives/ocular-disease-intelligent-recognition-using-hybrid-cnn-approach

*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.