Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/519570
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.coverage.spatial | Certain investigations on the performance analysis of lung tumor detection system | |
dc.date.accessioned | 2023-10-22T05:18:55Z | - |
dc.date.available | 2023-10-22T05:18:55Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/519570 | - |
dc.description.abstract | Even 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.extent | xvii, 129 p. | |
dc.language | English | |
dc.relation | p. 111-128 | |
dc.rights | university | |
dc.title | Certain investigations on the performance analysis of lung tumor detection system | |
dc.title.alternative | ||
dc.creator.researcher | Manoj Senthil K | |
dc.subject.keyword | ANFIS | |
dc.subject.keyword | CANFIS | |
dc.subject.keyword | Engineering | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Engineering Electrical and Electronic | |
dc.subject.keyword | Lung Tumor | |
dc.description.note | ||
dc.contributor.guide | Meeradevi T | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Electrical Engineering | |
dc.date.registered | ||
dc.date.completed | 2023 | |
dc.date.awarded | 2023 | |
dc.format.dimensions | 21 cm. | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Electrical Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 25.43 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 3.5 MB | Adobe PDF | View/Open | |
03_content.pdf | 480.77 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 128.21 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 340.9 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 173.08 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.02 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 666.96 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 536.09 kB | Adobe PDF | View/Open | |
10_annexures.pdf | 140.97 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 82.5 kB | Adobe PDF | View/Open |
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