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

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%.