  Background: Deep learning techniques have achieved high accuracy in image
classification tasks, and there is interest in applicability to neuroimaging
critical findings. This study evaluates the efficacy of 2D deep convolutional
neural networks (DCNNs) for detecting basal ganglia (BG) hemorrhage on
noncontrast head CT.
  Materials and Methods: 170 unique de-identified HIPAA-compliant noncontrast
head CTs were obtained, those with and without BG hemorrhage. 110 cases were
held-out for test, and 60 were split into training (45) and validation (15),
consisting of 20 right, 20 left, and 20 no BG hemorrhage. Data augmentation was
performed to increase size and variation of the training dataset by 48-fold.
Two DCNNs were used to classify the images-AlexNet and GoogLeNet-using
untrained networks and those pre-trained on ImageNet. Area under the curves
(AUC) for the receiver-operator characteristic (ROC) curves were calculated,
using the DeLong method for statistical comparison of ROCs.
  Results: The best performing model was the pre-trained augmented GoogLeNet,
which had an AUC of 1.00 in classification of hemorrhage. Preprocessing
augmentation increased accuracy for all networks (p<0.001), and pretrained
networks outperformed untrained ones (p<0.001) for the unaugmented models. The
best performing GoogLeNet model (AUC 1.00) outperformed the best performing
AlexNet model (AUC 0.95)(p=0.01).
  Conclusion: For this dataset, the best performing DCNN identified BG
hemorrhage on noncontrast head CT with an AUC of 1.00. Pretrained networks and
data augmentation increased classifier accuracy. Future prospective research
would be important to determine if the accuracy can be maintained on a larger
cohort of patients and for very small hemorrhages.
