Classification and Visualisation of Normal and Abnormal Radiographs; a comparison between Eleven Convolutional Neural Network Architectures

Abstract

Abstract 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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