| Creators: |
Zargari, Seyed Aman and Kia, Zahra Sadat and Nickfarjam, Ali Mohammad and Hieber, Daniel and Holl, Felix |
| Title: |
Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net |
| Item Type: |
Conference or Workshop Item |
| Event Title: |
(Proceedings of the) 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) |
| Event Location: |
Athens, Greece |
| Event Dates: |
July, 1-3, 2023 |
| Projects: |
DigiHealth |
| Page Range: |
pp. 93-96 |
| Additional Information: |
Open Access: Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0) |
| Date: |
2023 |
| Divisions: |
Gesundheitsmanagement |
| Abstract (ENG): |
We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net’s up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net’s up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors. |
| Forthcoming: |
No |
| Language: |
English |
| Uncontrolled Keywords: |
Deep Learning ; U-Net ; Multi-output Model ; Brain Tumor Segmentation ; Brain Tumor Classification |
| Link eMedia: |
Download |
| Citation: |
Zargari, Seyed Aman and Kia, Zahra Sadat and Nickfarjam, Ali Mohammad and Hieber, Daniel and Holl, Felix
(2023)
Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net.
In: (Proceedings of the) 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July, 1-3, 2023, Athens, Greece, pp. 93-96.
(Studies in Health Technology and Informatics; 305).
ISBN 9781643684017
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