Automatic Detection and Segmentation of Brain Hemorrhage Based on Improved U-Net Model

Thuong-Cang Phan; Anh-Cang Phan

doi:10.2174/1573405620666230915125635

Automatic Detection and Segmentation of Brain Hemorrhage Based on Improved U-Net Model

Curr Med Imaging. 2023 Sep 15. doi: 10.2174/1573405620666230915125635. Online ahead of print.

Authors

Thuong-Cang Phan¹, Anh-Cang Phan²

Affiliations

¹ College of Information and Communication Technology, Can Tho University, 94115 Can Tho, Vietnam.
² Faculty of Information Technology, Vinh Long University of Technology Education, 85110 Vinh Long, Vietnam.

PMID: 37724666
DOI: 10.2174/1573405620666230915125635

Abstract

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. 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. 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. 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%.

Keywords: Brain Hemorrhage; Brain Hemorrhage SegmentatioBrain Hemorrhage; Brain Hemorrhage Segmentation; DenseNet-121; MobileNet-V2; ResNet-50; U-Net.