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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Transfer Learning-based One Versus Rest Classifier for Multiclass Multi-Label Ophthalmological Disease Prediction

Author 1: Akanksha Bali
Author 2: Vibhakar Mansotra

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121269

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 12, 2021.

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Abstract: The main objective of this paper is to propose transfer learning technique for multiclass multilabel opthalmological diseases prediction in fundus images by using the one versus rest strategy. The proposed transfer learning-based techniques to detect eight categories (seven diseases and one normal class) are Normal, Diabetic retinopathy, Cataract, Glaucoma, Age-related macular degeneration, Myopia, Hypertension and Other abnormalities in fundus images collected and augmented from Ocular Disease Intelligent Recognition (ODIR) dataset. To increase the data set, no differentiation between left and right eye images has been done and these images were used on VGG-16 CNN network to binary classify each disease separately and trained 8 separate models using one versus rest strategy to identify these 7 diseases plus normal eyes. In this paper, various results has been showcased such as accuracy of each organ and accuracy of the overall model compared to benchmark papers. Base line accuracy have increased from 89% to almost 91% and also proposed model has improved the performance of identifying disease drastically prediction of glaucoma has increased from 54% to 91%, normal images prediction has increased from 40% to 85.28% and other diseases prediction has increased from 44% to 88%. Out of 8 categories prediction, proposed model prediction rate has improved in 6 diseases by using proposed transfer learning technique vgg16 and eight different one versus classifier classification algorithms.

Keywords: Fundus images; one versus rest strategy; transfer learning; VGG-16; augmentation

Akanksha Bali and Vibhakar Mansotra, “Transfer Learning-based One Versus Rest Classifier for Multiclass Multi-Label Ophthalmological Disease Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121269

@article{Bali2021,
title = {Transfer Learning-based One Versus Rest Classifier for Multiclass Multi-Label Ophthalmological Disease Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121269},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121269},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Akanksha Bali and Vibhakar Mansotra}
}


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