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Background: Deep learning techniques have achieved high accuracy in imageclassification tasks, and there is interest in applicability to neuroimagingcritical findings. This study evaluates the efficacy of 2D deep convolutionalneural networks (DCNNs) for detecting basal ganglia (BG) hemorrhage onnoncontrast head CT.  Materials and Methods: 170 unique de-identified HIPAA-compliant noncontrasthead CTs were obtained, those with and without BG hemorrhage. 110 cases wereheld-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 wasperformed to increase size and variation of the training dataset by 48-fold.Two DCNNs were used to classify the images-AlexNet and GoogLeNet-usinguntrained 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. Preprocessingaugmentation increased accuracy for all networks (p<0.001), and pretrainednetworks outperformed untrained ones (p<0.001) for the unaugmented models. Thebest performing GoogLeNet model (AUC 1.00) outperformed the best performingAlexNet model (AUC 0.95)(p=0.01).  Conclusion: For this dataset, the best performing DCNN identified BGhemorrhage on noncontrast head CT with an AUC of 1.00. Pretrained networks anddata augmentation increased classifier accuracy. Future prospective researchwould be important to determine if the accuracy can be maintained on a largercohort of patients and for very small hemorrhages.

Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks