Comparing Different Deep Learning Architectures for Classification of Chest Radiographs

Understand

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research.

• However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image-datasets.
• These datasets routinely differ from chest radiographs in that they are mostly color images and contain several possible image classes, while radiographs are greyscale images and often only contain fewer image classes.
• Therefore, very deep neural networks, which can represent more complex relationships in image-features, might not be required for the comparatively simpler task of classifying grayscale chest radiographs.