Laboratory Image Classification using Fuzzy Neural Algorithm:
Application for Colon Cancer Diagnosis
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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...
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 colorectalpolyps, extracts the required image features and then classifies the cells into normal and cancer respectively.
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