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Classification of cancerous cells images using clustered fuzzy-neural machine techniques

Lookup NU author(s): Ephraim Nwoye, Emeritus Professor Satnam Dlay, Dr Wai Lok Woo

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Abstract

Computer assisted diagnosis of cancer has received attention in recent years. The development of automated algorithms would be a valuable tool to the Pathologist for fast verification of these cancer abnormalities. In this paper a novel method which will automatically locate differences in cancer cells images and classy cells into normal and malignant is implemented by fuzzifying image feature descriptor values and incorporating clustering paradigm into neural network to classify images. The proposed system was evaluated using 116 cancers and 88 normal colon cells images. It is more efficient, simple to implement and yields better accuracy than conventional methods. (7 References).

Publication metadata

Author(s): Nwoye E, Dlay SS, Woo WL

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Communication Systems, Networks and Digital Signal Processing (CSNDSP)

Year of Conference: 2004

Pages: 491-494

Notes: Dlay SS Newcastle upon Tyne, UK. Communication Systems, Networks and Digital Signal Processing. CSNDSP 2004. Fourth International Symposium. Newcastle upon Tyne, UK. 20-22 July 2004.

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