Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/32856
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dc.contributor.authorLing, Sim Choon-
dc.contributor.authorAzian Azamimi, Abdulah-
dc.contributor.authorWan Khairunizam, Wan Ahmad, Dr.-
dc.date.accessioned2014-03-19T06:48:03Z-
dc.date.available2014-03-19T06:48:03Z-
dc.date.issued2014-01-
dc.identifier.citationAdvanced Science Letters, vol.20 (1), 2014, pages 254-258(5)en_US
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/32856-
dc.descriptionLink to publisher's homepage at http://www.aspbs.com/science.htmen_US
dc.description.abstractAn automation system using Cellular Neural Network (CNN) algorithm is proposed to assist the radiologists' task in interpreting the digital mammogram image for detecting the presence of breast masses abnormalities. Mammogram images are low contrast ergo; however by adopting the trained cellular neural network template, it is able to enhance the medical visual quality of the image significantly which is beneficial for early detection. By employing the trained CNN template, the extraction of abnormal breast lesion masses in digital mammogram image can be done efficiently with detection sensitivity up to 100 percent. Furthermore, a Matlab-based graphical user interface (GUI) is developed, featuring a user friendly concept which patient data and output images can be saved in the computer by the radiologists for future reference.en_US
dc.language.isoenen_US
dc.publisherAmerican Scientific Publishersen_US
dc.subjectCellular Neural Network (CNN)en_US
dc.subjectMammogramen_US
dc.subjectBreast canceren_US
dc.titleDesign of an automated breast cancer masses detection in mammogram using cellular neural network (CNN) algorithmen_US
dc.typeArticleen_US
Appears in Collections:Azian Azamimi Abdullah
School of Mechatronic Engineering (Articles)
Wan Khairunizam Wan Ahmad, Assoc. Prof. Ir. Ts. Dr.



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