Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

Autor: João Manuel R. S. Tavares, Clayton R. Pereira, Pedro Pedrosa Rebouças Filho, Victor Hugo C. de Albuquerque, João Paulo Papa, Antônio Carlos da Silva Barros, Geraldo Luis Bezerra Ramalho
Přispěvatelé: Faculdade de Engenharia, Inst Fed Fed Educ Ciencia & Tecnol Ceara IFCE, Universidade Estadual Paulista (Unesp), Univ Fortaleza, Univ Porto
Rok vydání: 2017
Předmět:
0209 industrial biotechnology
Image classification
Computer science
Feature extraction
Pulmonary disease
02 engineering and technology
Machine learning
computer.software_genre
Ciências Tecnológicas
Ciências médicas e da saúde
020901 industrial engineering & automation
Artificial Intelligence
Robustness (computer science)
Fibrosis
0202 electrical engineering
electronic engineering
information engineering
medicine
Medical imaging
Asthma
COPD
Lung
Contextual image classification
business.industry
Ciências médicas e da saúde
medicine.disease
Support vector machine
medicine.anatomical_structure
Lung disease
Technological sciences
Medical and Health sciences
Optimum-path forest
Medical and Health sciences
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Classifier (UML)
Software
Zdroj: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP
Web of Science
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-017-3048-y
Popis: Made available in DSpace on 2019-10-05T22:02:15Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-01 Graduate Program in Computer Science from the Federal Institute of Education, Science and Technology of Ceara Department of Computer Engineering from the Walter Cantidio University Hospital of the Federal University of Ceara, in Brazil Federal Institute of Education, Science and Technology of Ceara through grant PROINFRA/2013 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Project SciTech-Science and Technology for Competitive and Sustainable Industries Programa Operacional Regional do Norte (NORTE2020), through Fundo Europeu de Desenvolvimento Regional (FEDER) Federal Institute of Education, Science and Technology of Ceara through grant PROAPP/2014 The World Health Organization estimated that around 300 million people have asthma, and 210 million people are affected by Chronic Obstructive Pulmonary Disease (COPD). Also, it is estimated that the number of deaths from COPD increased 30% in 2015 and COPD will become the third major cause of death worldwide by 2030. These statistics about lung diseases get worse when one considers fibrosis, calcifications and other diseases. For the public health system, the early and accurate diagnosis of any pulmonary disease is mandatory for effective treatments and prevention of further deaths. In this sense, this work consists in using information from lung images to identify and classify lung diseases. Two steps are required to achieve these goals: automatically extraction of representative image features of the lungs and recognition of the possible disease using a computational classifier. As to the first step, this work proposes an approach that combines Spatial Interdependence Matrix (SIM) and Visual Information Fidelity (VIF). Concerning the second step, we propose to employ a Gaussian-based distance to be used together with the optimum-path forest (OPF) classifier to classify the lungs under study as normal or with fibrosis, or even affected by COPD. Moreover, to confirm the robustness of OPF in this classification problem, we also considered Support Vector Machines and a Multilayer Perceptron Neural Network for comparison purposes. Overall, the results confirmed the good performance of the OPF configured with the Gaussian distance when applied to SIM- and VIF-based features. The performance scores achieved by the OPF classifier were as follows: average accuracy of 98.2%, total processing time of 117 microseconds in a common personal laptop, and F-score of 95.2% for the three classification classes. These results showed that OPF is a very competitive classifier, and suitable to be used for lung disease classification. Inst Fed Fed Educ Ciencia & Tecnol Ceara IFCE, Lab Processamento Digital Imagens & Simulacao Com, Campus Maracanau, Maracanau, Ceara, Brazil Univ Estadual Paulista, Dept Ciencia Comp, Bauru, SP, Brazil Univ Fortaleza, Programa Posgrad Informat Aplicada, Fortaleza, Ceara, Brazil Univ Porto, Fac Engn, Dept Engn Mecan, Inst Ciencia & Inovaco Engn Mecan & Engn Ind, Porto, Portugal Univ Estadual Paulista, Dept Ciencia Comp, Bauru, SP, Brazil CNPq: 470501/2013-8 CNPq: 301928/2014-2 CNPq: 306166/2014-3 FAPESP: 2014/16250-9 FAPESP: 2014/12236-1 Project SciTech-Science and Technology for Competitive and Sustainable Industries: NORTE-01-0145-FEDER-000022
Databáze: OpenAIRE
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