An Internet of Medical Things Based Liver Tumor Detection System using Semantic Segmentation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Durrani, M.Y. | - |
dc.contributor.author | Yasmin, S. | - |
dc.contributor.author | Rho, Seungmin | - |
dc.date.accessioned | 2023-03-08T08:10:34Z | - |
dc.date.available | 2023-03-08T08:10:34Z | - |
dc.date.issued | 2022-03 | - |
dc.identifier.issn | 1607-9264 | - |
dc.identifier.issn | 2079-4029 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61519 | - |
dc.description.abstract | Internet of Medical Things (IOMT) based systems provide a framework for remote health monitoring. Liver tumors tend to have parallel intensities with neighboring lesions and may have an abnormal apparent form that directly depends on the stage, state, type, and luminosity setup. In this research, a segmentation model based on improved UNet has been deployed to segment the tumors by incorporating the side-byside convolution layers based on Filter Response normalization layers (FRN) along with Threshold Linear Units (TLU). This combination of FRN along with TLU has a very strong impact on the performance of the model as the FRN layer operates on each batch sample and each response filter during training, and thus it eliminates the problem of batch dependence. Furthermore, we have also switched from the traditional up-sampling layers to fractionally strided convolutions in UNet which performs up-sampling of the required image with proper learning. Moreover, the tumors are directly segmented by the proposed framework from the given CT scan without any extraction of ROIs. To evaluate the performance of our proposed method, we use a publicly available 3DIRCADb dataset. The proposed technique has shown excellent results with 93.0% accuracy and 71.2% Jaccard score. © 2022 Taiwan Academic Network Management Committee. All rights reserved. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Taiwan Academic Network Management Committee | - |
dc.title | An Internet of Medical Things Based Liver Tumor Detection System using Semantic Segmentation | - |
dc.type | Article | - |
dc.identifier.doi | 10.53106/160792642022032302015 | - |
dc.identifier.bibliographicCitation | Journal of Internet Technology, v.23, no.2, pp 363 - 375 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000778147500016 | - |
dc.identifier.scopusid | 2-s2.0-85128253503 | - |
dc.citation.endPage | 375 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 363 | - |
dc.citation.title | Journal of Internet Technology | - |
dc.citation.volume | 23 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대만 | - |
dc.subject.keywordAuthor | IOMT | - |
dc.subject.keywordAuthor | Liver tumor detection | - |
dc.subject.keywordAuthor | Medical decision making | - |
dc.subject.keywordAuthor | Semantic segmentation | - |
dc.subject.keywordAuthor | U-Net | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | CT | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.