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Multi-Criteria Decision-Making Techniques for Histopathological Image Classification

Multi-Criteria Decision-Making Techniques for Histopathological Image Classification
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Author(s): Revathi T. (Mepco Schlenk Engineering College, India), Saroja S. (Mepco Schlenk Engineering College, India), Haseena S. (Mepco Schlenk Engineering College, India) and Blessa Binolin Pepsi M. (Mepco Schlenk Engineering College, India)
Copyright: 2019
Pages: 36
Source title: Histopathological Image Analysis in Medical Decision Making
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, India), Amira S. Ashour (Tanta University, Egypt), Harihar Kalia (Seemantha Engineering College, India), R.T. Goswami (Techno India College of Technology, India) and Himansu Das (KIIT University, India)
DOI: 10.4018/978-1-5225-6316-7.ch005

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Abstract

This chapter presents an overview of methods that have been proposed for analysis of histopathological images. Diagnosing and detecting abnormalities in medical images helps the pathologist in making better decisions. Different machine learning algorithms such as k-nearest neighbor, random forest, support vector machine, ensemble learning, multilayer perceptron, and convolutional neural network are incorporated for carrying out the analysis process. Further, multi-criteria decision-making (MCDM) methods such as SAW, WPM, and TOPSIS are used to improve the efficiency of the decision-making process.

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