A Hybrid Automatic System for the Diagnosis of Lung Cancer Based on Genetic Algorithm and Fuzzy Extreme Learning Machines

An automatic system for the diagnosis of lung cancer has been proposed in this manuscript. The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM) for the classification of lung cancer dat...

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Bibliographic Details
Published in:Journal of medical systems Vol. 36; no. 2; pp. 1001 - 1005
Main Author: Daliri, Mohammad Reza
Format: Journal Article
Language:English
Published: Boston Springer US 01.04.2012
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Automation
Cancer
Diagnosis
Fuzzy
Fuzzy Logic
Fuzzy set theory
Genetic algorithms
Health Informatics
Health Sciences
Humans
Hybridization
Image Processing, Computer-Assisted - methods
Learning
Lung cancer
Lung Neoplasms - diagnosis
Lungs
Medical diagnosis
Medicine
Medicine & Public Health
Original Paper
Statistics for Life Sciences
Extreme Learning Machines (ELM)
Fuzzy Inference System (FIS)
Genetic Algorithm (GA)
Lung Cancer
Diagnosis
Automatic Medical System
ISSN:0148-5598, 1573-689X
Online Access:Get full text
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Summary:An automatic system for the diagnosis of lung cancer has been proposed in this manuscript. The proposed method is based on combination of genetic algorithm (GA) for the feature selection and newly proposed approach, namely the extreme learning machines (ELM) for the classification of lung cancer data. The dimension of the feature space is reduced by the GA in this scheme and the effective features are selected in this way. The data are then fed to a fuzzy inference system (FIS) which is trained by the fuzzy extreme learning machines approach. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications.
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ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-011-9806-y