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

Ananda, A., Ngan, K. H. ORCID: 0000-0001-7623-942X, Karabag, C., Ter-Sarkisov, A., Alonso, E. ORCID: 0000-0002-3306-695X and Reyes-Aldasoro, C. C. ORCID: 0000-0002-9466-2018 (2021). Classification and Visualisation of Normal and Abnormal Radiographs: a comparison between Eleven Convolutional Neural Network Architectures. Sensors, 21(16), 5381. doi: 10.3390/s21165381

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.

Publication Type: Article
Additional Information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher Keywords: Wrist Fractures; Radiographic Images; Classification; Convolutional Neural Networks; Class Activation Mapping
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Mathematics, Computer Science & Engineering > Computer Science > giCentre
Date available in CRO: 02 Aug 2021 08:46
Date deposited: 2 August 2021
Date of acceptance: 1 August 2021
Date of first online publication: 9 August 2021
URI: https://openaccess.city.ac.uk/id/eprint/26534
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