An Optimized Deep Learning Model with Enhanced Padding for Early Detection of Retinal Diseases

Authors

  • Ch. Swapna, Kasa Susmitha, Bandaru Hemaza, Adudodla Thanusri, Akuthota Aravind

Keywords:

Keywords: Retinal disease detection, Deep learning, Convolutional Neural Network (CNN), Diabetic Retinopathy (DR), Medical image classification.

Abstract

This research presents an AI-based automated diagnosis system designed to detect eye diseases fromretinal images with high accuracy and efficiency. The system integrates advanced deep learningtechniques with a user-friendly graphical interface to provide clinicians and researchers with a powerfultool for early disease detection and analysis

References

Jolly, Lochan, Niket Amoda, and K. Mishra. "Artificial Intelligence-Enabled IOMT for Medical Application." In Handbook of Research on Artificial Intelligence and Soft Computing Methods in Personalized Healthcare Services, pp. 3-32. Apple Academic Press, 2024.

Nadhan, Archana S., and I. Jeena Jacob. "Enhancing healthcare security in the digital era: Safeguarding medical images with lightweight cryptographic methods in IoT healthcare applications." Biomedical Signal Processing and Control 88 (2024): 105511.

Downloads

Published

2025-04-07

How to Cite

Ch. Swapna, Kasa Susmitha, Bandaru Hemaza, Adudodla Thanusri, Akuthota Aravind. (2025). An Optimized Deep Learning Model with Enhanced Padding for Early Detection of Retinal Diseases . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 462–481. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2319

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.