The way in which the most recent studies approach the processing of the samples, distinguishing between the use of guided methods versus the analysis of the raw data by an artificial intelligence model, is the most relevant aspect analyzed in this work.
Published in Chapter:
Evolution and Tendency on the Feature Extraction Process for Diagnostic Aid in Healthcare
Elena Escobar-Linero (Universidad de Sevilla, Spain), Luis Muñoz-Saavedra (Universidad de Sevilla, Spain), Francisco Luna-Perejón (Universidad de Sevilla, Spain), Javier Civit-Masot (Universisdad de Sevilla, Spain), Manuel Rivas-Pérez (Universidad de Sevilla, Spain), Manuel Domínguez-Morales (Universidad de Sevilla, Spain), and Anton Civit Balcells (Universisdad de Sevilla, Spain)
Copyright: © 2023
|Pages: 45
DOI: 10.4018/978-1-6684-6434-2.ch006
Abstract
Diagnostic support systems based on artificial intelligence are of great interest in the field of healthcare since they offer an early and highly accurate diagnosis. In recent years, the use of machine learning models that are less complex than the most widely used ones, such as deep neural networks, has been of interest. For this purpose, it is necessary to apply previous steps of feature extraction of different types, according to the problem that has to be solved. In this chapter, the authors intend to provide an overview of the current panorama of these systems, differentiating between those that extract features and those that do not. An exhaustive analysis is made of the works carried out in the last five years, checking the artificial intelligence models used, the features extracted, and the countries responsible for each work.