Exploring the Impact of Image Resolution on Chest X-ray Classification Performance
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Deep learning models for image classification have often used a resolution of 224×224 pixels for computational reasons. This study investigates the effect of image resolution on chest X-ray classification performance, using the ChestX-ray14 dataset. The results show that a higher image resolution, specifically 1024×1024 pixels, has the best overall classification performance, with a slight decline in performance between 256×256 to 512×512 pixels for most of the pathological classes. Comparison of saliency map-generated bounding boxes revealed that commonly used resolutions are insufficient for finding most pathologies.
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