Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519570
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dc.coverage.spatialCertain investigations on the performance analysis of lung tumor detection system
dc.date.accessioned2023-10-22T05:18:55Z-
dc.date.available2023-10-22T05:18:55Z-
dc.identifier.urihttp://hdl.handle.net/10603/519570-
dc.description.abstractEven though, the machine learning algorithms for tumor region detection in lung CT images obtain optimum tumor detection accuracy, they require large number of external features for training the machine learning classification algorithm designed. Further, these algorithms are not suitable for the diagnosis of segmented tumor region due to the requirements of large number of lung CT images. To overcome such limitations in the conventional machine learning algorithms, the deep learning algorithm is proposed in this chapter for tumor region detection and diagnosis in lung CT images. The diagnosed abnormal images are further classified as Earlyand#8223; and Advancedand#8223; stages. Experiments carried out in this research work uses LIDC open access dataset. The proposed lung tumor detection and classification method also is analyzed with respect to deep learning algorithm, ANFIS and CANFIS classification approaches.
dc.format.extentxvii, 129 p.
dc.languageEnglish
dc.relationp. 111-128
dc.rightsuniversity
dc.titleCertain investigations on the performance analysis of lung tumor detection system
dc.title.alternative
dc.creator.researcherManoj Senthil K
dc.subject.keywordANFIS
dc.subject.keywordCANFIS
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Electrical and Electronic
dc.subject.keywordLung Tumor
dc.description.note
dc.contributor.guideMeeradevi T
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Electrical Engineering
dc.date.registered
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions21 cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Electrical Engineering

Files in This Item:
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01_title.pdfAttached File25.43 kBAdobe PDFView/Open
02_prelim_pages.pdf3.5 MBAdobe PDFView/Open
03_content.pdf480.77 kBAdobe PDFView/Open
04_abstract.pdf128.21 kBAdobe PDFView/Open
05_chapter 1.pdf340.9 kBAdobe PDFView/Open
06_chapter 2.pdf173.08 kBAdobe PDFView/Open
07_chapter 3.pdf1.02 MBAdobe PDFView/Open
08_chapter 4.pdf666.96 kBAdobe PDFView/Open
09_chapter 5.pdf536.09 kBAdobe PDFView/Open
10_annexures.pdf140.97 kBAdobe PDFView/Open
80_recommendation.pdf82.5 kBAdobe PDFView/Open


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