Work

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

Year: 2024

Type: article

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.

Source: ELCVIA Electronic Letters on Computer Vision and Image Analysis

DOI: 10.5565/rev/elcvia.1755

Authors: Mohankrishna Potnuru, B. Suribabu Naick

Institution: GITAM University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Neurology

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4404724327

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