Classification of Pulmonary Nodules by Using Hybrid Features-Reference-Cited by-同舟云学术

Classification of Pulmonary Nodules by Using Hybrid Features

Published:2013 Issue: Volume:2013 Page:1-11
ISSN:1748-670X
Container-title:Computational and Mathematical Methods in Medicine
language:en
Short-container-title:Computational and Mathematical Methods in Medicine

Author:

Tartar Ahmet¹,Kilic Niyazi²,Akan Aydin²^ORCID

Affiliation:

1. Department of Engineering Sciences, Istanbul University, 34320 Avcılar, Istanbul, Turkey

2. Department of Electrical and Electronics Engineering, Istanbul University, 34320 Avcılar, Istanbul, Turkey

Abstract

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

Funder

Scientific Research Projects Coordination Unit of Istanbul University

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modelling and Simulation,General Medicine

Link

http://downloads.hindawi.com/journals/cmmm/2013/148363.pdf

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