Laboratory Image Classification using Fuzzy Neural Algorithm: | Application for Colon Cancer Diagnosis

Ephraim Nwoye

ISBN 10: 3659949132 / ISBN 13: 9783659949135

Published by LAP LAMBERT Academic Publishing, 2016

About this Item

Laboratory Image Classification using Fuzzy Neural Algorithm: | Application for Colon Cancer Diagnosis | Ephraim Nwoye | Taschenbuch | 128 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659949135 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 103390003

Bibliographic Details

• Title: Laboratory Image Classification using Fuzzy Neural Algorithm: | Application for Colon Cancer Diagnosis
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 3659949132
• ISBN 13: 9783659949135
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 209 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Pathologists, daily, screen large numbers of slides containing cancerous cells manually. These are similar in shape, size or cells structure.This procedure or process then becomes arduous, difficult and can affect their judgement and decisions resulting in wrong diagnosis. Therefore development of an automated algorithmic approach, based on quantitative measurements, would be a valuable aid to the pathologist to verify abnormalities. The main aim of this book is therefore to use a neural network approach together with fuzzy arithmetic to establish a relationship between normal and cancer colon cell structures. This relationship is of high significance as it will result in an automated tool for accurate diagnosis. A novel Fast Fuzzy Neural Back-propagation Algorithm (FFNBA) for classification of colon cell images is therefore proposed. The algorithm used an optimal learning method for three layers MLP. The method automatically detects differences in biopsy images of the colorectal polyps, extracts the required image features and then classifies the cells into normal and cancer respectively.

About the Author

Ephraim Nwoye holds a PhD in Electrical and ElectronicEngineering from the Newcastle University,United Kingdom andcurrently a professor of Biomedical Engineering at the University of Lagos, Nigeria with research interests in medical imaging, image & signal processing and biomedical instrumentation.