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  • Open Access

    ARTICLE

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

    Anandhavalli Muniasamy1,*, 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

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

  • Open Access

    ARTICLE

    ECGAN: Translate Real World to Cartoon Style Using Enhanced Cartoon Generative Adversarial Network

    Yixin Tang*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182 - 08 June 2023

    Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training… More >

  • Open Access

    ARTICLE

    Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

    Hassam Tahir1, Muhammad Shahbaz Khan1, Fawad Ahmed2, Abdullah M. Albarrak3, Sultan Noman Qasem3, Jawad Ahmad4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3517-3535, 2023, DOI:10.32604/cmc.2023.035410 - 31 March 2023

    Abstract The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated… More >

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