Neural Machine for classification of cancerous cell

dc.contributor.author	Nwoye, E. O.
dc.contributor.author	Dlay, S. S.
dc.contributor.author	Woo, W. L.
dc.contributor.author	Maghani, K. A.
dc.date.accessioned	2019-09-27T07:36:04Z
dc.date.available	2019-09-27T07:36:04Z
dc.date.issued	2003-07-01
dc.description.abstract	Computer assisted diagnosis of colorectal cancer has received attention in recent years. The development of an automated algorithmic approach, based on quantitative measurements, would be a valuable tool to the Pathologist for fast verification of these colon cancer abnormalities for effective treatment. In this paper novel method which will automatically locate differences in colon cell Images and classy the colon cell into normal and malignant cells is presented. This system is implemented by fuzzifying image feature descriptor fractals and incorporating clustering paradigm with neural network to classify images. The proposed system was evaluated using 116 cancers and 88normal colon cells images and shown to be more efficient, simple to implement and yield better accuracy than conventional methods	en_US
dc.identifier.issn	1109-2777
dc.identifier.uri	https://ir.unilag.edu.ng/handle/123456789/6205
dc.language.iso	en	en_US
dc.publisher	WSEAS Transactions. on Systems	en_US
dc.relation.ispartofseries	Vol. 2(3);655-659
dc.subject	Fuzzy logic	en_US
dc.subject	Neura Network	en_US
dc.subject	Clustering Algorithms	en_US
dc.subject	Cancerous Cells	en_US
dc.title	Neural Machine for classification of cancerous cell	en_US
dc.type	Article	en_US

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