Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures

Autor: Ananda Ananda, Kwun Ho Ngan, Cefa Karabağ, Aram Ter-Sarkisov, Eduardo Alonso, Constantino Carlos Reyes-Aldasoro
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Sensors, Vol 21, Iss 16, p 5381 (2021)
Druh dokumentu: article
ISSN: 1424-8220
DOI: 10.3390/s21165381
Popis: This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). 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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