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Early and accurate diagnosis of Intracranial Hemorrhage (ICH) has a great clinical significance for timely treatment. In this study, we proposed a deep learning method for automatic ICH diagnosis. We exploited three windowing levels to enhance different tissue contrasts to be used for feature extraction. Our convolutional neural network (CNN) model employed the EfficientNet-B2 architecture and was re-trained using a published annotated computer tomography (CT) image dataset of ICH. The performance of our model has the overall accuracy of 0.973 and precision of 0.965. The processing time is less than 0.5 second per image slice.
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Deep Learning Method for Intracranial Hemorrhage Detection and Subtype Differentiation
Early and accurate diagnosis of Intracranial Hemorrhage (ICH) has a great clinical significance for timely treatment. In this study, we proposed a deep learning method for automatic ICH diagnosis. We exploited three windowing levels to enhance different tissue contrasts to be used for feature extraction. Our convolutional neural network (CNN) model employed the EfficientNet-B2 architecture and was re-trained using a published annotated computer tomography (CT) image dataset of ICH. The performance of our model has the overall accuracy of 0.973 and precision of 0.965. The processing time is less than 0.5 second per image slice.