Abstract
This study primarily focuses on a novel approach to Covid-19 prediction utilizing X-ray images. The images are used for the initial stage of training of the CNN Convolution neural network model. For improved classification and prediction accuracy, images are trained and tested using a hybrid GANs (Generative Adversarial Networks based Convolution neural network) - CNN (Convulational Neural Network) model. The noise cancellation technique of image processing has been used to minimize the noise in images and used for the GANs-CNN hybrid model. Each method of the proposed model has resulted in better accuracy, in which the validation accuracy on every 15 epochs is 79.2%, 85.8%, and 87.1% respectively.
COVID-19 Disease Prediction using GANs-CNN Model.
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Anil Kumar, K., Neupane, B., Malla, S., Pandey, D.P. (2024). COVID-19 Disease Prediction Using Generative Adversarial Networks with Convolutional Neural Network (GANs-CNN) Model. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_12
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DOI: https://doi.org/10.1007/978-3-031-53085-2_12
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