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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Identification of complex shapes using a self organizing neural system.

T Sabisch¹, A Ferguson, H Bolouri

¹Engineering Research and Development Centre, University of Hertfordshire, Hatfield AL10 9AB, UK.

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

A novel multilayer hierarchical neural system enhances complex contour pattern classification. This advanced neural network structure shows superior tolerance to pattern distortion compared to standard methods.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Automatic classification of complex patterns is crucial in various fields.
Standard neural networks often struggle with pattern distortion.

Purpose of the Study:

To introduce a multilayer hierarchical neural system for improved contour pattern classification.
To evaluate the system's performance against standard neural network implementations.

Main Methods:

Development of a neocognitron-like network structure.
Integration of self-organizing maps for automatic feature class determination.
Implementation of a multilayer hierarchical architecture.

Main Results:

The proposed system achieves automatic classification of complex contour patterns.
Demonstrated superior tolerance to pattern distortion compared to standard neural networks.
Successful determination of feature classes using self-organizing maps.

Conclusions:

Multilayer hierarchical neural networks offer significant advantages for pattern recognition tasks.
The developed system provides a robust solution for classifying distorted patterns.
This approach advances the field of automatic pattern classification.