Lung disease detection using feature extraction and extreme learning machine
Rev. bras. eng. biomed
; 30(3): 207-214, Sept. 2014. ilus, tab
Article
em En
| LILACS
| ID: lil-723257
Biblioteca responsável:
BR1.1
ABSTRACT
INTRODUCTION:
The World Health Organization estimates that by 2030 the Chronic Obstructive Pulmonary Disease (COPD) will be the third leading cause of death worldwide. Computerized Tomography (CT) images of lungs comprise a number of structures that are relevant for pulmonary disease diagnosis and analysis.METHODS:
In this paper, we employ the Adaptive Crisp Active Contour Models (ACACM) for lung structure segmentation. And we propose a novel method for lung disease detection based on feature extraction of ACACM segmented images within the cooccurrence statistics framework. The spatial interdependence matrix (SIM) synthesizes the structural information of lung image structures in terms of three attributes. Finally, we perform a classification experiment on this set of attributes to discriminate two types of lung diseases and health lungs. We evaluate the discrimination ability of the proposed lung image descriptors using an extreme learning machine neural network (ELMNN) comprising 4-10 neurons in the hidden layer and 3 neurons in the output layer to map each pulmonary condition. This network was trained and validated by applying a holdout procedure.RESULTS:
The experimental results achieved 96% accuracy demonstrating the effectiveness of the proposed method on identifying normal lungs and diseases as COPD and fibrosis.CONCLUSION:
Our results lead to conclude that the method is suitable to integrate clinical decision support systems for pulmonary screening and diagnosis.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
LILACS
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Rev. bras. eng. biomed
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2014
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Brasil