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Hodges, J. (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, chapter Nonparametric Discrimination: Small sample performance. IEEE Computer Society Press, Los Alamitos, Reprint of original work from 1952.

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• TITLE: Carcinoma cell identification via optical microscopy and shape feature analysis

  AUTHORS: Ahmad Chaddad, Camel Tanougast, Andrew Golato, Abbas Dandache

  KEYWORDS: Cancer; Carcinoma; Detection; Shape Features

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.11, November 11, 2013

  ABSTRACT: Optical microscopy is commonly used for cancer cell detection. Focusing on carcinoma cell identification via optical microscopy, a proof-of-concept study was performed at Laboratory of Design, Optimization and Modeling (LCOMS) to determine the grade of cancer cells. This paper focuses on three types of abnormal cells; namely, Benign Hyperplasia (BH), Intraepithelial Neoplasia (IN), which is a precursor state for cancer, and Carcinoma (Ca), which corresponds to abnormal tissue proliferation cancer. These types of cells were used to assess the efficiency of using shape features to identify carcinoma cells. A comparative study based on performance indicator concludes that three features, Area, Xor-Convex, and Solidity, were found to be effective in identifying the Carcinoma grade of cancer cells.