2023
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 ,
Jonathan D. Cruz*
- Universidad de Investigación de Tecnología Experimental Yachay, Urcuquí, Ecuador
- Manuscript receivedOctober 10, 2022
- revisedNovember 13, 2022
- acceptedDecember 9, 2022
- publishedMay 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
How to 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. https://doi.org/10.12720/jait.14.3.392-398
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
