Dr DAH-Unet: A modified UNet for Semantic Segmentation of MRI images for brain tumour detection

Abstract

Using sophisticated image processing techniques on brain MR images for medical image segmentation significantly improves the ability to detect tumors. It takes a lot of time and requires a doctor's training and experience to manually segment a brain tumor. To address this issue, we proposed a modification in Unet architecture called DAH-Unet that combines residual blocks, a rebuilt atrous spatial pyramid pooling (ASPP), and depth-wise convolutions. Also, a hybrid loss function which is explicitly aware of the boundaries is another thing we suggested. Experiments were conducted on two publicly available dataset and proved better in some metrics as compare to existing semantic segmentation models.

Keywords

• Semantic segmentation
• UNet architecture
• DAH-Unet
• Brain tumour Detection
• high-grade glioma