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Explainable Diagnosis of COVID-19 from Chest X-ray Images via CNNs
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2021
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Gesellschaft für Informatik, Bonn
Zusammenfassung
This work demonstrates how Convolutional Neural Networks ( CNN s) can be used to identify signs of COVID-19 from Chest X-rays (CXR s) and discusses the challenges of deep learning with small datasets. In order to validate the model’s performance, two novel explanation methods LIME and Grad-CAM are explored. Additionally, they serve to further increase users’ confidence in specific classifications. Since the explanation results revealed model biases, additional preprocessing mechanisms were explored: A U-Net-based lung segmenter is introduced to the preprocessing pipeline, which masks all non-lung parts of the CXRs images. Subsequently, the segmentation and non-segmentation results were evaluated with regard to both their performance metrics and interpreted explanation results.