Abstract
To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect of multiple views. Data augmentation was performed during training. The Inception V3, Resnet, and Xception convolutional neural networks were constructed utilizing the Python programming language with Tensorflow as the framework. Training was performed using single radiographic views. Measured output metrics were accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. Model outputs were evaluated using both one and three radiographic views. Ensembles were created from a combination of CNNs after training. A voting method was implemented to consolidate the output from the three views and model ensemble. For single radiographic views, the ensemble of all 5 models produced the best accuracy at 76%. When all three views for a single case were utilized, the ensemble of all models resulted in the best output metrics with an accuracy of 81%. Despite our small dataset size, by utilizing an ensemble of models and 3 views for each case, we achieved an accuracy of 81%, which was in line with the accuracy of other models using a much higher number of cases with pre-trained models and models which implemented manual feature extraction.
Highlights
Several recent studies have demonstrated the utility of machine learning for fracture detection in musculoskeletal images
Training convolutional neural network (CNN) models require the selection of multiple hyperparameters, which are tunable parametersin the model and are often kept close to the values determined by the original implementation
The ensemble consisting of all five models produced the best fracture detection accuracy of 76% for the validation-test set
Summary
Several recent studies have demonstrated the utility of machine learning for fracture detection in musculoskeletal images. One study performed manual feature extraction on 145 radiographs, utilized a random forest machine learning algorithm, and achieved a fracture detection accuracy of 81% [1]. There are currently many readily available open-source implementations of CNNs through frameworks such as Caffe [5] Many of these models are pre-trained on the dataset from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) containing millions of non-medical images [6]. When using pre-trained models, only the last layer or two of the CNN are retrained on the dataset of interest, but the rest of the model is kept unchanged These pre-trained networks have been shown to be good feature extractors, and one study achieved a fracture detection accuracy of 83% utilizing them with ~ 256,000 wrist, hand, and ankle radiographs [7]. The input channel for the pre-trained models are set at 3, so each grayscale image with 1 channel needs to be triplicated before being
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