Dr DAH-Unet : A modified UNet for Semantic Segmentation of MRI images for brain tumour detection
Potnuru, Mohankrishna (GITAM University (India))
Suribabu Naick, B. (GITAM University (India))

Data:	2024
Resum:	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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Semantic segmentation ; U-Net architecture ; DAH-U-net ; Brain tumour Detection ; High-grade glioma
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 2 (2024) , p. 29-49 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1755
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001031
DOI: 10.5565/rev/elcvia.1755
DOI: 10.5565/rev/elcvia.2322024

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