Classification of Lungs Nodules using Hybrid Features and Neural Network

Lungs nodule detection and classification is a very crucial step for computer aided diagnosis (CAD) systems. In this paper, the authors have proposed a CAD system that consists of multiple phases. In the first phase, Lungs segmentation has been performed. After that, region of interest ROl that cont...

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Published inInternational Information Institute (Tokyo). Information Vol. 17; no. 5; p. 1771
Main Authors Jaffar, M Arfan, Choi, Wook-Jin, Choi, Tae-Sun
Format Journal Article
LanguageEnglish
Published Koganei International Information Institute 01.05.2014
Subjects
Boundaries
Classification
Computer aided design
Consortia
Lung cancer
Lungs
Medical imaging
Neural networks
Nodules
Phases
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Summary:Lungs nodule detection and classification is a very crucial step for computer aided diagnosis (CAD) systems. In this paper, the authors have proposed a CAD system that consists of multiple phases. In the first phase, Lungs segmentation has been performed. After that, region of interest ROl that contains nodule has been extracted. Different types of features have been extracted for nodule classification. Artificial Neural Network has been used for classification. The technique was tested on the dataset Lungs Image Database Consortium LIDC and images taken from Aga Khan Medical University. The results confirm the validity of the technique, as well as its enhanced performance.
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ISSN:1343-4500
1344-8994