Multiclass classification for tissue characterization

Atam P. Dhawan

doi:10.1142/9789812814807_0019

Multiclass classification for tissue characterization

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Computer aided diagnostic applications such as cancer detection may require a binary classification into benign and malignant classes. However, there are many medical imaging applications requiring multiclass classifications to categorize image data into more than two classes for tissue or pathology characterization. This chapter provides an introduction of some of the approaches such as Bayesian classification, support vector machine, and neuro-fuzzy systems that can be applied in multiclass classification.

Original language	English (US)
Title of host publication	Principles and Advanced Methods in Medical Imaging and Image Analysis
Publisher	World Scientific Publishing Co.
Pages	455-480
Number of pages	26
ISBN (Electronic)	9789812814807
ISBN (Print)	9789812705341
DOIs	https://doi.org/10.1142/9789812814807_0019
State	Published - Jan 1 2008

All Science Journal Classification (ASJC) codes

General Medicine

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10.1142/9789812814807_0019

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abstract = "Computer aided diagnostic applications such as cancer detection may require a binary classification into benign and malignant classes. However, there are many medical imaging applications requiring multiclass classifications to categorize image data into more than two classes for tissue or pathology characterization. This chapter provides an introduction of some of the approaches such as Bayesian classification, support vector machine, and neuro-fuzzy systems that can be applied in multiclass classification.",

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Multiclass classification for tissue characterization

Abstract

All Science Journal Classification (ASJC) codes

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