Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tahir, Ayesha Bin T. | - |
dc.contributor.author | Khan, Muhamamd Attique | - |
dc.contributor.author | Alhaisoni, Majed | - |
dc.contributor.author | Khan, Junaid Ali | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.contributor.author | Wang, Shui-Hua | - |
dc.contributor.author | Javed, Kashif | - |
dc.date.accessioned | 2021-08-11T08:31:11Z | - |
dc.date.available | 2021-08-11T08:31:11Z | - |
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/2197 | - |
dc.description.abstract | Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused the features of both layers into a single, more informative vector. An IPSO algorithm selected the optimal features, which were classified using a support vector machine. Results: We analyzed high- and low-grade glioma images from the BRATS 2018 dataset; the identification accuracies were 99.9% and 99.3%, respectively. Impact: The accuracy of our method is significantly higher than existing techniques; thus, it will help radiologists to make diagnoses, by providing a "second opinion." | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2021.015154 | - |
dc.identifier.scopusid | 2-s2.0-85103625328 | - |
dc.identifier.wosid | 000632946200013 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.68, no.1, pp 1099 - 1116 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 68 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1099 | - |
dc.citation.endPage | 1116 | - |
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.keywordPlus | FEATURES FUSION | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | NETWORK | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordAuthor | Brain tumor | - |
dc.subject.keywordAuthor | contrast enhancement | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | classification | - |
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