Brain MRI images segmentation based on U-net architecture
Joint Authors
Atiyyah, Asalah Dhaki
Ali, Khawlah Husayn
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 18, Issue 1 (30 Jun. 2022), pp.21-27, 7 p.
Publisher
University of Basrah College of Engineering
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Brain tumors are collections of abnormal tissues within the brain.
the regular function of the brain may be affected as it grows within the region of the skull.
brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them.
the diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task.
brain tumor segmentation must be carried out automatically.
a proposed strategy for brain tumor segmentation is developed in this paper.
for this purpose, images are segmented based on region-based and edge-based.
brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study.
a comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed.
the edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0.008768, IoU score of 0.7542, f 1 score of 0.9870, the accuracy of 0.9935, the precision of 0.9852, recall of 0.9888, and specificity of 0.9951.
American Psychological Association (APA)
Atiyyah, Asalah Dhaki& Ali, Khawlah Husayn. 2022. Brain MRI images segmentation based on U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 18, no. 1, pp.21-27.
https://search.emarefa.net/detail/BIM-1380205
Modern Language Association (MLA)
Atiyyah, Asalah Dhaki& Ali, Khawlah Husayn. Brain MRI images segmentation based on U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering Vol. 18, no. 1 (Jun. 2022), pp.21-27.
https://search.emarefa.net/detail/BIM-1380205
American Medical Association (AMA)
Atiyyah, Asalah Dhaki& Ali, Khawlah Husayn. Brain MRI images segmentation based on U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering. 2022. Vol. 18, no. 1, pp.21-27.
https://search.emarefa.net/detail/BIM-1380205
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 27
Record ID
BIM-1380205