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https://hdl.handle.net/10356/46985
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DC Field | Value | Language |
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dc.contributor.author | Lim, Wei Keat | en_US |
dc.date.accessioned | 2011-12-27T05:52:08Z | |
dc.date.available | 2011-12-27T05:52:08Z | |
dc.date.copyright | 2004 | en_US |
dc.date.issued | 2004 | |
dc.identifier.uri | http://hdl.handle.net/10356/46985 | |
dc.description | 92 p. | en_US |
dc.description.abstract | Breast cancer is the most common cancer affecting Singapore women. Every year, 700 - 1000 new cases of breast cancer are diagnosed and about 250 women die from the disease. High-quality mammography, an X-ray technique that vi-sualizes the internal structure of the breast, is the most effective early detection technology currently available. It is sensitive to screening and diagnosis. Un-fortunately, it results in a high false-positive rate, i.e. only a small portion of masses found on mammograms are malignant. Recent studies found that only 20-30% mammographically suspicious nonpalpable breast masses were malig-nant. The National Cancer Institute of America also reported that 80% of American women who undergo surgical breast biopsies do not have cancers. Considering the cost of biopsy and the patient's emotional disturbance, it is crucial to develop high-performance computer-aided diagnosis (CAD) systems to assist radiologists in the classification of breast cancers in order to reduce unnecessary biopsies. | en_US |
dc.rights | Nanyang Technological University | en_US |
dc.subject | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence | en_US |
dc.title | Development of an intelligent system for breast cancer diagnosis | en_US |
dc.type | Thesis | en_US |
dc.contributor.supervisor | Er Meng Joo | en_US |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.description.degree | Master of Engineering | en_US |
item.grantfulltext | restricted | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | EEE Theses |
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File | Description | Size | Format | |
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EEE_THESES_4.pdf Restricted Access | 9.36 MB | Adobe PDF | View/Open |
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