Cart
Create Account
Sign In
Already purchased this program?
Login to View
This video program is a part of the Premium package:
Deep Learning Method for Intracranial Hemorrhage Detection and Subtype Differentiation
- IEEE MemberUS $11.00
- Society MemberUS $0.00
- IEEE Student MemberUS $11.00
- Non-IEEE MemberUS $15.00
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.
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.
