Anandhavalli Muniasamy^1,*, Ashwag Alasmari²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 569-592, 2025, DOI:10.32604/cmes.2025.060484 - 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. More > Graphic Abstract

Yujian Wang¹, Jianxun Zhang^1,*, Renhao Sun²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 533-548, 2024, DOI:10.32604/cmc.2024.054644 - 15 October 2024

Abstract The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency on extensive labeled data, a cold-start problem persists in small to medium-sized expression recognition datasets. This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics. This paper introduces an active learning approach that integrates uncertainty estimation, aiming to improve the precision of facial… More >

Karim Gasmi^1,*, Lassaad Ben Ammar^2,, Hmoud Elshammari⁴, Fadwa Yahya²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270 - 31 March 2023

Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and… More >