Provides an image-based environment which includes acquisition, diagnosis, and interpretation from the generated digitized data.
Published in Chapter:
Multi-Criteria Decision-Making Techniques for Histopathological Image Classification
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
DOI: 10.4018/978-1-5225-6316-7.ch005
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.