Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/593674
Full metadata record
DC FieldValueLanguage
dc.coverage.spatialAutomatic detection and classification of liver cancer based on neural network techniques
dc.date.accessioned2024-10-04T07:07:18Z-
dc.date.available2024-10-04T07:07:18Z-
dc.identifier.urihttp://hdl.handle.net/10603/593674-
dc.description.abstractCancer is the leading cause of mortality in every region of the globe. newlineThe early detection and diagnosis of liver cancer is one of the most vitally newlineimportant parts of cancer prevention. In clinical radiology, two of the difficult newlineproblems to solve in liver cancer are finding it early and correctly classifying newlineit. The objective is to detect liver cancer at an early stage in order to save as newlinemany lives as possible. newlineThe objective of this research is to develop an automated method that newlinecan reliably identify and categorize liver cancers utilizing an image processing newlinesystem. This method will be of use to the radiologist in automatically newlinerecognizing any lesions that have occurred in the liver. Because, manually newlinedetecting cancer tissue is a tedious and time-consuming activity. Computer newlineAided Diagnosis (CAD) has been greatly employed to assess and analyze liver newlinecancer in order to accurately and rapidly diagnose the disease. The Computed newlineTomography (CT) scan has emerged as an essential diagnostic tool for liver newlinecancer. newlineThe primary motive of this research is to develop a method that may newlinebe used for classifying and dividing liver cancer into several subtypes. The liver newlinewas autonomously segmented from the full abdominal CT scan image by using newlineactive contour, level set and PSO algorithms. The features were extracted from newlinethe liver that had been segmented once the segmentation process was complete. newlineThese generated features were used in the classification of liver cancer by the newlineapplication of techniques such as Random Forest (RF), Feed Forward Neural newlineNetwork (FFNN) and Back Propagation Neural Network (BPNN). newline
dc.format.extentxvi,141p.
dc.languageEnglish
dc.relationp.129-140
dc.rightsuniversity
dc.titleAutomatic detection and classification of liver cancer based on neural network techniques
dc.title.alternative
dc.creator.researcherAgita, T K R
dc.subject.keywordclinical radiology
dc.subject.keyworddiagnosis of liver cancer
dc.subject.keywordEngineering
dc.subject.keywordEngineering and Technology
dc.subject.keywordEngineering Biomedical
dc.subject.keywordregion of the globe
dc.description.note
dc.contributor.guideMoorthi, M
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2024
dc.date.awarded2024
dc.format.dimensions21cm.
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File25.48 kBAdobe PDFView/Open
02_prelim pages.pdf1.69 MBAdobe PDFView/Open
03_content.pdf15.03 kBAdobe PDFView/Open
04_abstract.pdf7.93 kBAdobe PDFView/Open
05_chapter1.pdf299.44 kBAdobe PDFView/Open
06_chapter2.pdf196.93 kBAdobe PDFView/Open
07_chapter3.pdf1 MBAdobe PDFView/Open
08_chapter4.pdf574.4 kBAdobe PDFView/Open
09_chapter5.pdf405.16 kBAdobe PDFView/Open
10_chapter6.pdf404.93 kBAdobe PDFView/Open
11_chapter7.pdf373.9 kBAdobe PDFView/Open
12_anneuxres.pdf149.73 kBAdobe PDFView/Open
80_recommendation.pdf58.44 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: