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Abstract #1372

Differentiation of Vertebral Fracture Types using Five Different Convolutional Neural Network Approaches

Lee-Ren Yeh1, Yang Zhang2, Jeon-Hor Chen1,2, Peter Chang2, Daniel Chow2, and Min-Ying Lydia Su2

1Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan, 2Department of Radiological Sciences, University of California, Irvine, CA, United States

Differentiation of benign from malignant vertebral fracture is challenging yet very important for therapeutic planning. In this study, deep learning was conducted to automatically differentiate the fracture types using 5 different convolutional neural networks, including ResNet50, DenseNet, Xception, xceptionResNetV2, and InceptionV3. The final segmentation model was developed using 10-fold cross-validation applied in two different input methods, i.e. single slice or each slice combined with its two neighboring slices. Overall, the prediction accuracy was improved when each slice combined with its two neighboring slices was used as the input. Among the five deep learning approaches, XceptionResnetV2 showed the highest prediction accuracy.

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