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

作　　者：Anandhavalli Muniasamy Ashwag Alasmari 

机构地区：[1]Department of Informatics and Computer Systems,College of Computer Science,King KhalidUniversity,Abha,61421,Saudi Arabia [2]Department of Computer Science,King Khalid University,Abha,61421,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期569-592,共24页工程与科学中的计算机建模(英文)

基　　金：Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.

摘　　要：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 thusmake 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 fromKaggle.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.

关 键 词：Bayesian neural networks(BNNs) convolution neural networks(CNN) Bayesian convolution neural networks(BCNNs) predictive modeling precision medicine uncertainty quantification 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]