Title:Automatic Detection and Segmentation of Brain Hemorrhage based on
Improved U-Net Model
Volume: 20
Author(s): Thuong-Cang Phan and Anh-Cang Phan*
Affiliation:
- Faculty of Information Technology, Vinh Long University of Technology Education, 85110 Vinh Long, Vietnam
Keywords:
Brain hemorrhage, U-Net, DenseNet-121, ResNet-50, MobileNet-V2, Brain hemorrhage segmentation.
Abstract:
Introduction:
Brain hemorrhage is one of the leading causes of death due to the sudden rupture of a blood vessel in the brain, resulting in bleeding in the brain
parenchyma. The early detection and segmentation of brain damage are extremely important for prompt treatment.
Methods:
Some previous studies focused on localizing cerebral hemorrhage based on bounding boxes without specifying specific damage regions. However,
in practice, doctors need to detect and segment the hemorrhage area more accurately. In this paper, we propose a method for automatic brain
hemorrhage detection and segmentation using the proposed network models, which are improved from the U-Net by changing its backbone with
typical feature extraction networks, i.e., DenseNet-121, ResNet-50, and MobileNet-V2. The U-Net architecture has many outstanding advantages.
Results:
It does not need to do too many preprocessing techniques on the original images and it can be trained with a small dataset providing low error
segmentation in medical images. We use the transfer learning approach with the head CT dataset gathered on Kaggle including two classes,
bleeding and non-bleeding.
Conclusion:
Besides, we give some comparison results between the proposed models and the previous works to provide an overview of the suitable model for
cerebral CT images. On the head CT dataset, our proposed models achieve a segmentation accuracy of up to 99%.
