Identifying the Best Image Classification Algorithm for COVID-19 Diagnosis with a Small, Imbalanced Chest X-Ray Dataset

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Abstract With more than 82 million COVID-19 cases worldwide [1], automated chest radiograph interpretation could provide substantial benefit for efficient and accurate diagnosis of COVID-19 patients. In this project, families of deep learning neural networks are trained on publicly available chest X-ray datasets to identify the best image classification algorithm for automating the diagnosis of […]