Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net...21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July 1-3, 2023, Athens, Greece.
We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and upsampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use full...
| Published in: | Studies in Health Technology & Informatics Vol. 305; pp. 93 - 97 |
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| Main Authors: | , , , , |
| Format: | diagnostic images equations & formulas proceedings tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164789441&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164789441 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 305 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 164789441 164789441 164789441 10.3233/SHTI230432 164789441 ppf: 93 ppct: 4 formats: tig: atl: Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net...21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH), July 1-3, 2023, Athens, Greece. aug: au: ZARGARI, Seyed Aman KIA, Zahra Sadat NICKFARJAM, Ali Mohammad HIEBER, Daniel HOLL, Felix affil: Electrical Engineering Department, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. sug: subj: Brain Neoplasms Classification Brain Neoplasms Radiography Neural Networks (Computer) Deep Learning Image Processing, Computer Assisted Methods Congresses and Conferences Greece Greece Machine Learning Magnetic Resonance Imaging ab: We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and upsampling. 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas proceedings tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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