3D Semantic Deep Learning Networks for Leukemia Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Amin, Javaria | - |
dc.contributor.author | Sharif, Muhammad | - |
dc.contributor.author | Anjum, Muhammad Almas | - |
dc.contributor.author | Siddiqa, Ayesha | - |
dc.contributor.author | Kadry, Seifedine | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Raza, Mudassar | - |
dc.date.accessioned | 2021-09-10T06:27:11Z | - |
dc.date.available | 2021-09-10T06:27:11Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.issn | 1546-2226 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19087 | - |
dc.description.abstract | White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body's immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness. | - |
dc.format.extent | 15 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | 3D Semantic Deep Learning Networks for Leukemia Detection | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2021.015249 | - |
dc.identifier.scopusid | 2-s2.0-85107834438 | - |
dc.identifier.wosid | 000659131200001 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.69, no.1, pp 785 - 799 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 69 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 785 | - |
dc.citation.endPage | 799 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordAuthor | YOLOv2 | - |
dc.subject.keywordAuthor | darknet53 | - |
dc.subject.keywordAuthor | Bhattacharyya separately criteria | - |
dc.subject.keywordAuthor | ONNX | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG 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.