Deep learning for COVID-19 by X-ray images Analysis and Designing Diagnostic Application
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Abstract
Nearly every element of life is being significantly impacted by the COVID-19 pandemic. Since COVID-19 was just recently identified, there isn't much known about the illness, how to identify it, or how to treat it. This has increased interest in the creation of AI-based automated detection systems, and deep learning is a group of machine learning algorithms used in AI that aim to automatically extract key properties from a dataset. Based on their architecture and learning principles, these neural networks are classified into a number of groups. Artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN) are a few of the popular deep learning categories. Deep learning is therefore a powerful tool that could be used to classify data in ways that humans might not be able to. This makes it possible for computers to learn from modest quantities of data and provide excellent outcomes. For the first task in this investigation, multiple Convolutional neural networks (CNN) models were used.To maximize accuracy during training with maintain constant conditions. After training on x-ray images, VGG16, VGG19, and ResNet50V2, were found to ResNet50V2 have the highest accuracy (96%) and can be used in future chest x-ray studies and applications. Second, we designed a COVID-19 Diagnostic application. This app uses a chest x-ray to determine if a person has the disease or is healthy, saving medical staff time and energy and helping with preventative isolation. Test huge numbers from images in record time.
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