MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Majumder, Surya | - |
dc.contributor.author | Gautam, Nandita | - |
dc.contributor.author | Basu, Abhishek | - |
dc.contributor.author | Sau, Arup | - |
dc.contributor.author | Geem, Zong Woo | - |
dc.contributor.author | Sarkar, Ram | - |
dc.date.accessioned | 2024-05-07T13:00:20Z | - |
dc.date.available | 2024-05-07T13:00:20Z | - |
dc.date.issued | 2024-03 | - |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91133 | - |
dc.description.abstract | Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.title | MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans | - |
dc.type | Article | - |
dc.identifier.wosid | 001192136700028 | - |
dc.identifier.doi | 10.1371/journal.pone.0298527 | - |
dc.identifier.bibliographicCitation | PLOS ONE, v.19, no.3 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85187561624 | - |
dc.citation.title | PLOS ONE | - |
dc.citation.volume | 19 | - |
dc.citation.number | 3 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordPlus | NODULES | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | EQUATION | - |
dc.subject.keywordPlus | TEXTURE | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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