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Author: search.filters.author.Andrés Tirado-EspínSubject: search.filters.subject.COVID-19

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    Topological Analysis Techniques for Improving Neural Network Performance in COVID-19 Detection Using Persistent Homology
    (2024)
    Israel Reyes
    ;
    Karen Cáceres-Benítez
    ;
    Ana Marcillo
    ;
    Andre Vera
    ;
    Carolina Cadena-Morejón
    In this study, we employ topological data analysis techniques on neural networks applied in COVID-19 detection, aiming to improve their predictive power. Leveraging the power of persistent homology, a mathematical tool for extracting topological features from intricate datasets, we turned chest X-ray images into a representation of the topological features. This representation was used to train and test the ability of neural networks to learn topological properties from images. We examine neural networks trained on chest radiographs containing both COVID-19 positive and negative cases. Our results suggest that, by identifying specific topological features correlated with COVID-19 detection, we may enhance the performance of the neural network models and analyze the underlying factors contributing to high accuracy rate of detection. The findings from this study contribute as exploratory advance in the field of medical imaging analysis and disease detection, showcasing the potential of topological analysis within neural networks.
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    Using Artificial Intelligence and X-ray Images to Train and Predict COVID-19 and Pneumonia: Tool for Diagnosis and Treatment
    (2025)
    Bryan Juárez-Gonzalez
    ;
    Fernando Villalba-Meneses
    ;
    Jonathan Cruz-Varela
    ;
    Andrés Tirado-Espín
    ;
    Paulina Vizcaino-Imacaña
    In January 2022, Ecuador experienced a peak in COVID-19 cases, with 890,541 confirmed cases and 35,658 deaths. During 2019-2020, influenza and pneumonia also ranked among the top causes of death. Traditional chest X-rays and chest CT scans are commonly used for diagnosing COVID-19, but studies by Wong et al. indicate that chest X-rays are less sensitive compared to CT scans unless artificial intelligence is utilized. Tahir et al. highlighted that AI models such as U-net and neural networks achieve high diagnostic accuracy, with U-Net++ and ResNet18 models showing sensitivities above 99% and perfect specificity using a large dataset of 33,920 chest X-ray images. The rapid detection of symptoms could have helped in prioritizing critical care, potentially reducing deaths. In the current study, it is demonstrated that combining AI with chest X-rays can achieve a binary accuracy above 98% for COVID-19 detection using transfer learning with Xception, VGG16, and VGG19 neural networks. Using 27,052 chest X-ray images, the VGG19 model achieved an excellent F1-score of 98.53% for COVID-19 and normal class classification. The VGG19 model also performed well in multiclass classification with an F1-score above 89%. The study concludes that AI-enhanced diagnostic tools, such as VGG19, are valuable for hospitals in diagnosing COVID-19 and future improvements might include larger datasets and enhanced segmentation techniques.
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