Classification of Atypical Femur Fracture with Deep Neural Networks
Abstract: Atypical Femur Fracture(AFF) is a type of stress fracture that occurs in conjunction with prolonged bisphosphonate treatment. In practice, AFF is very rarely identiﬁed from Normal Femur Fracture(NFF) correctly on the ﬁrst diagnostic X-ray examination. This project aims at developing an algorithm based on deep neural networks to assist clinicians with the diagnosis of atypical femurfracture. Two diagnostic pipelines were constructed using the Convolutional Neural Network (CNN) as the core classiﬁer. One is a fully automatic pipeline, where the X-rays image is directly input into the network with only standardized pre-processing steps. Another interactive pipeline requires the user to re-orient the femur bones above the fractures to a vertical position and move the fracture line to the image center, before the repositioned image is sent to the CNNs. Three most popular CNNs architectures, namely VGG19, InceptionV3 and ResNet50,were tested for classifying the images to either AFF or NFF. Transfer learning technique was used to pre-train these networks using images form ImageNet. The diagnosis accuracy was evaluated using 5-fold cross-validation. With the fully automatic diagnosis pipeline, we achieved diagnosis accuracy of 82.7%, 89.4%, 90.5%, with VGG19, InceptionV3 and ResNet50, respectively. With the interactive diagnostic pipeline, the diagnosis accuracy was improved to 92.2%, 93.4% and 94.4%, respectively. To further validate the results, class activation mapping is used for indicating the discriminative image regions that the neural networks learn to identify a certain class.
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