Abstract
To classify and detect the presence of pneumonia or COVID-19 in a series of chest X-ray images, this research presents a convolutional neural network model that was trained from the ground up. Many other methods use transfer learning or handcrafted methods to attain a high level of accuracy in classifying data. Pneumonia or COVID-19 can be diagnosed using a convolutional neural network model that extracts information from the provided chest X-ray image and classifies it. The reliability and interpretability issues that arise frequently when dealing with medical images may be alleviated by using this method. Pneumonia datasets are scarce for this classification assignment, as they are for other deep learning classification tasks that require a huge image library. To increase the CNN model's validation and classification accuracy, we applied a variety of data augmentation strategies. We were able to achieve outstanding validation accuracy.
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