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Title: Certain analysis on lung cancer CT image segmentation and classification using ANN and SVM techniques for early
Researcher: Kavitha M S
Guide(s): Shanthini J
Keywords: Life Sciences
Biology and Biochemistry
Mathematical and Computational Biology
CT image segmentation
ANN techniques
SVM techniques
University: Anna University
Completed Date: 2019
Abstract: In medical applications medical images are one of the shared resources to find the exact disorder or diseases observation Image processing is an imperative branch in bio medical applications as this process is required in elaboration and distinguishing of pixels originating from numerous image sources for example Computed Tomography CT scans X rays Magnetic resonance MR imaging etc Exceptions are information which can be viewed as a typical because of a few causes e g abnormal process conditions Hence the image processing helps to identify and detect such anomaly things like cancer that limit the effect of special cases in the last model to make or as a preliminary pre taking care of stage As per literature image processing in medical field determines how cancer is to be cured what are all the methods that must adapt to detect cancer in easy way Further the system model implements the concept of improving the acquisition filtering and extraction modules specifically for cancer The reason to opt for cancer detection is due to the recent changes in health factor and it depends upon the polluted environment religious traditions customary practices and improper food factors On the other hand in recent surveys cancer is determined in various parts of human body The detection mechanism which is available results in serious limitations like compatibility is missing between each cancer analyses In terms of improving such aspects and detection of cancer with high accuracy is must Past methodologies present weaknesses hence in the recent years numerous commitments have been proposed to beat them and enhance the nature of the detection Established strategies are regularly not reasonable to treat some specific databases along these lines late examinations have been directed on exception location for these sorts of dataset newline
Pagination: xvii,122p.
Appears in Departments:Faculty of Technology

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04_acknowledgements.pdf277.38 kBAdobe PDFView/Open
05_contents.pdf169.11 kBAdobe PDFView/Open
06_listoftables.pdf112.18 kBAdobe PDFView/Open
07_listoffigures.pdf130.78 kBAdobe PDFView/Open
08_listofabbreviations.pdf177.14 kBAdobe PDFView/Open
09_chapter1.pdf1.18 MBAdobe PDFView/Open
10_chapter2.pdf1.07 MBAdobe PDFView/Open
11_chapter3.pdf1.36 MBAdobe PDFView/Open
12_chapter4.pdf1.32 MBAdobe PDFView/Open
13_chapter5.pdf1.39 MBAdobe PDFView/Open
14_conclusion.pdf148.07 kBAdobe PDFView/Open
15_references.pdf984.35 kBAdobe PDFView/Open
16_listofpublications.pdf206.33 kBAdobe PDFView/Open
80_recommendation.pdf135.87 kBAdobe PDFView/Open

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