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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Detecting Prominent Textural Features for Cervical Cancer Lesions Using Outlier Detection and Removal Techniques

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Author S.Pradeep Kumar Kenny, S.Allwin
ISSN 2079-8407
On Pages 412-420
Volume No. 5
Issue No. 5
Issue Date June 1, 2014
Publishing Date June 1, 2014
Keywords Cervical Cytology, Cervical Cancer, Outlier detection, Outlier removal, features, classification


Cervical cyto images have developed profound interest among research enthusiasts in the recent past. The reason for this is not only because of its killer reputation but also due to the diverse challenging unsolved problem it possess. Cervical cancer like all other cancer develops through various stages before it actually causes potential harm. These stages can be detected through visual information such as the shape, texture etc., by a pathologist. However there aren’t so many pathologists around to do the job leaving the alternative choice to an automated system to detect these features. However since each image has different properties, finding a profound set of features which can be used to detect cancer for a particular stage of cancer is still a problem. In an automated system detecting the stage of cancer using a feature for a particular stage of cancer is not going to work. A combination of features can work but finding the right combination is a challenge. Too many features in the combination will drastically slow the system and too little will affect the accuracy of the system. To strike a balance between the two a unique technique using outlier detection and removal is used to isolate the best feature that can be used to detect a particular stage of cancer.

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