Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/226175
Title: Neural network based architecture for mammogram classification using genetic algorithm
Researcher: Valarmathi P
Guide(s): Robinson S
Keywords: Engineering and Technology,Computer Science,Computer Science Theory and Methods
Genetic Algorithm
Mammogram
Neural Network
University: Anna University
Completed Date: 2017
Abstract: Each year around the world millions of women develop new cases of newlinebreast cancer Mammograms can be used to check for breast cancer in women newlinewho have no signs or symptoms of the disease Screening mammograms newlineusually involve two x ray pictures or images of each breast Screening newlinemammograms can also find Micro CalCifications MCCs a tiny deposits of newlinecalcium that sometimes indicate the presence of breast cancer newlineDiagnostic Mammogram can also be used to check for breast cancer newlineafter a lump or other sign or symptom of the disease. If calcifications are newlinegrouped together in a certain way it may be a sign of cancer If the MCCs newlinehave a suspicious look and pattern a biopsy will be recommended For newlineMCCs the interpretations of their presence are very difficult because of its newlinemorphological features The dense tissues especially in younger women may newlineeasily be misinterpreted as MCCs due to film emulsion error digitization newlineartefacts or anatomical structures such as fibrous strands breast borders or newlinehypertrophied lobules that almost similar to MCCs newline newline
Pagination: xvii, 133p.
URI: http://hdl.handle.net/10603/226175
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf884.21 kBAdobe PDFView/Open
03_abstract.pdf57.21 kBAdobe PDFView/Open
04_acknowledgements.pdf4.88 kBAdobe PDFView/Open
05_contents.pdf114.79 kBAdobe PDFView/Open
06_list_of_tables.pdf4.45 kBAdobe PDFView/Open
07_list_of_figures.pdf8.85 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf9.5 kBAdobe PDFView/Open
09_chapter1.pdf145.05 kBAdobe PDFView/Open
10_chapter2.pdf169.21 kBAdobe PDFView/Open
11_chapter3.pdf394.11 kBAdobe PDFView/Open
12_chapter4.pdf398.81 kBAdobe PDFView/Open
13_chapter5.pdf382.85 kBAdobe PDFView/Open
14_conclusion.pdf62.44 kBAdobe PDFView/Open
15_references.pdf118.62 kBAdobe PDFView/Open
16_list_of_publications.pdf60.19 kBAdobe PDFView/Open
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