Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ullah, Naeem | - |
dc.contributor.author | Hassan, Muhammad | - |
dc.contributor.author | Khan, Javed Ali | - |
dc.contributor.author | Anwar, Muhammad Shahid | - |
dc.contributor.author | Aurangzeb, Khursheed | - |
dc.date.accessioned | 2024-03-15T12:00:26Z | - |
dc.date.available | 2024-03-15T12:00:26Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 0899-9457 | - |
dc.identifier.issn | 1098-1098 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90704 | - |
dc.description.abstract | Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error-prone and time-consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub-image dualistic histogram equalization (DSIHE) for enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation for feature extraction, followed by a fully connected classification layer. Transparency and interpretability are emphasized through the application of the Local Interpretable Model-Agnostic Explanations (LIME) method to explain model predictions. Results demonstrate DeepEBTDNet's efficacy in brain tumor detection, even across datasets, achieving a validation accuracy of 98.96% and testing accuracy of 94.0%. This study underscores the importance of explainable AI in healthcare, facilitating precise diagnoses and transparent decision-making for early brain tumor identification and improved patient outcomes. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | WILEY | - |
dc.title | Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images | - |
dc.type | Article | - |
dc.identifier.wosid | 001129452400001 | - |
dc.identifier.doi | 10.1002/ima.23012 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, v.34, no.1 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85180203912 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY | - |
dc.citation.volume | 34 | - |
dc.citation.number | 1 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | brain-tumor detection | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | explainable AI | - |
dc.subject.keywordAuthor | LIME | - |
dc.subject.keywordAuthor | MRI | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Optics | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Optics | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.