Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/253117
Full metadata record
DC FieldValueLanguage
dc.coverage.spatialClassification of Breast Cancer with Mammogram Images using Various Transformations and Machine Learning Techniques-
dc.date.accessioned2019-08-19T12:49:50Z-
dc.date.available2019-08-19T12:49:50Z-
dc.identifier.urihttp://hdl.handle.net/10603/253117-
dc.description.abstractBreast cancer is one of the leading diseases for women in the world. It is ranked second among all types of cancers, to cause death in women. The root cause of breast cancer is not known still now. So, there are no proper preventive measures for this killer disease. But the early detection is necessary to reduce the mortality rate. The early detection of breast cancer and treatment leads to an increase in the survival rate of women. Mammography is a standard radiological screening technique, which is used for early detection of breast cancer. Nowadays, mammogram is examined by newlineradiologists to find the abnormal regions. However, due to several reasons second reading is needed to get more accurate results. As a result, early detection of breast cancer is achieved through the development and usage of Computer Aided Diagnosis (CAD) system. From the previous research, it is clear that, more number of computer aided system have been developed and evaluated in order to achieve the better classification accuracy of breast cancer. Still, it is important and necessary to increase the efficiency and the classification accuracy of the CAD system for breast cancer detection. Hence, the computational framework is developed for the breast cancer diagnosis and classification, which has an improved efficiency over the other existing methods. The radiologists use this developed computational framework as the second reader without any manual interruption and coding knowledge. newline newline-
dc.format.extentxxi, 147p.-
dc.languageEnglish-
dc.relationp.134-146-
dc.rightsuniversity-
dc.titleQos based network selection scheme for wlan/wimax integrated networks with velocity and sar consideration-
dc.creator.researcherChandra, Sekaran S-
dc.subject.keywordBreast Cancer-
dc.subject.keywordEngineering and Technology,Computer Science,Computer Science Interdisciplinary Applications-
dc.subject.keywordMammogram-
dc.subject.keywordMammogram Images-
dc.subject.keywordTransformations-
dc.contributor.guideG. Tholkappia Arasu-
dc.publisher.placeChennai-
dc.publisher.universityAnna University-
dc.publisher.institutionFaculty of Information and Communication Engineering-
dc.date.registeredn.d.-
dc.date.completed2018-
dc.date.awarded30/04/2018-
dc.format.dimensions21 cm-
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 File23.09 kBAdobe PDFView/Open
02_certificates.pdf308.44 kBAdobe PDFView/Open
03_abstract.pdf7.5 kBAdobe PDFView/Open
04_acknowledgment.pdf4.61 kBAdobe PDFView/Open
05_contents.pdf91.58 kBAdobe PDFView/Open
06_chapter1.pdf987.2 kBAdobe PDFView/Open
07_chapter2.pdf209.5 kBAdobe PDFView/Open
08_chapter3.pdf162.84 kBAdobe PDFView/Open
09_chapter4.pdf534.06 kBAdobe PDFView/Open
10_chapter5.pdf393.3 kBAdobe PDFView/Open
11_conclusion.pdf50.45 kBAdobe PDFView/Open
12_references.pdf38.39 kBAdobe PDFView/Open
13_publications.pdf14.83 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: