Home > Published Issues > 2023 > Volume 14, No. 3, 2023 >
JAIT 2023 Vol.14(3): 392-398
doi: 10.12720/jait.14.3.392-398

Multi-class Classification Approach for Retinal Diseases

Mario G. Gualsaqui, Stefany M. Cuenca, Ibeth L. Rosero, Diego A. Almeida, Carolina Cadena, Fernando Villalba, and Jonathan D. Cruz *
Universidad de Investigación de Tecnología Experimental Yachay, Urcuquí, Ecuador;
Email: mario.gualsaqui@yachaytech.edu.ec (M.G.G.), stefany.cuenca@yachaytech.edu.ec (S.M.C.), ibeth.rosero@yachaytech.edu.ec (I.L.R.), dalmeida@yachaytech.edu.ec (D.A.A.), ccadena@yachaytech.edu.ec (C.C.), gvillalba@yachaytech.edu.ec (F.V.)
*Correspondence: jcruz@yachaytech.edu.ec (J.D.C.)

Manuscript received October 10, 2022; revised November 23, 2022, accepted December 9, 2022; published May 5, 2023.

Abstract—Early detection of the diagnosis of some diseases in the retina of the eye can improve the chances of cure and also prevent blindness. In this study, a Convolutional Neural Network (CNN) with different architectures (Scratch Model, GoogleNet, VGG, ResNet, MobileNet and DenseNet) was created to make a comparison between them and find the one with the best percentage of accuracy and less loss to generate the model for a better automatic classification of images using a MURED database containing retinal images already labeled previously with their respective disease. The results show that the model with the ResNet architecture variant InceptionResNetV2 has an accuracy of 49.85%.
 
Keywords—retinal diagnosis, Convolutional Neural Network (CNN), deep learning, machine learning, automated diagnosis

Cite: Mario G. Gualsaqui, Stefany M. Cuenca, Ibeth L. Rosero, Diego A. Almeida, Carolina Cadena, Fernando Villalba, and Jonathan D. Cruz, "Multi-class Classification Approach for Retinal Diseases," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 392-398, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.

PREVIOUS PAPER
First page
NEXT PAPER
Spelling Check: A New Cognition-Inspired Sequence Learning Memory