Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures ( ). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes-normal and abnormal. The arch...
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Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2021-08, Vol.21 (16), p.5381 |
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Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (
). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes-normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen's kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs. |
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ISSN: | 1424-8220 1424-8220 |
DOI: | 10.3390/s21165381 |