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Classification of Atypical Femur Fracture with Deep Neural Networks
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Klassificering av atypisk femurfraktur med djupa neuronnät (Swedish)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. , p. 28
Series
TRITA-CBH-GRU ; 2019:082
Keywords [en]
deep neural networks, classification, atypical femur fracture, class activation mapping
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255677OAI: oai:DiVA.org:kth-255677DiVA, id: diva2:1341053
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Presentation
2019-06-04, T63, Hälsovägen 11C 141 57 HUDDINGE, Stockholm, 09:00 (English)
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-07 Last updated: 2019-08-15Bibliographically approved

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