Eye disease detection using EEG signals

Registration ID: IJNRD_217080

Published ID: IJNRD2404571

DOI: Click Here to Get

Vishwa Doshi , Aitha Vishal , P. Velmurugan

Machine learning, neural network, convolutional neural networks, deep learning, EEG

The crucial and difficult problem of detecting ocular illnesses from the EEG signals of the human eye has important therapeutic implications. This work explores the use of deep learning algorithms—more especially, EfficientNet B2—for precise and automated diagnosis of ocular diseases. Our method uses image processing and convolutional neural networks to deliver a stable and effective solution. We use a large dataset of EEG scans that covers all phases and types of eye diseases to train and evaluate the model. Some of the research's major achievements include creating a comprehensive pipeline to prepare EEG data, refining the EfficientNet B2 architecture, and thoroughly analysing the model's performance. Our findings show the potential for accurate, sensitive, and precise classification of eye disorders. Deep learning algorithms to improve patient care and management by helping clinicians with early and accurate diagnosis

Published Paper   E-Certificate

Vishwa Doshi, Aitha Vishal, & P. Velmurugan (April-2024). Eye disease detection using EEG signals. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), f579-f586. https://ijnrd.org/papers/IJNRD2404571.pdf

Volume 9 Issue 4, April-2024

Pages : f579-f586

Paper Reg. ID: IJNRD_217080

Published Paper Id: IJNRD2404571

Downloads: 000121979

Research Area: Engineering

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404571.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404571

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper - Volume 10 | Issue 9 | September 2025

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Current Issue: Volume 10 | Issue 9 | September 2025

Impact Factor: 8.76

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