Open Access

ARTICLE

Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images

Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

1 Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
2 Department of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia

* Corresponding Author: Anandhavalli Muniasamy. Email:

(This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)

Computer Modeling in Engineering & Sciences 2025, 143(1), 569-592. https://doi.org/10.32604/cmes.2025.060484

Received 02 November 2024; Accepted 17 February 2025; Issue published 11 April 2025

Abstract

The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model. This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images from Kaggle. The deep CNN model has an accuracy of 95%, while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions. When compared with other methods, the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.

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