Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images

Authors

Sheeraz Akram, Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanFollow
Muhammad Younus Javed, Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanFollow
Usman Qamar, Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanFollow
Aasia Khanum, Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanFollow
Ali Hassan, Department of Computer Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, PakistanFollow

Author Country (or Countries)

Pakistan

Abstract

An automated pulmonary nodule detection system is necessary to help radiologist to identify and detect the nodules at early stage. In this paper, a novel pulmonary nodule detection system is proposed using Artificial Neural Networks (ANN) based on hybrid features consist of 2D and 3D Geometric and Intensity based statistical features. The lung volume is segmented using thresholding, 3D connected component labeling, contour correction and morphological operators. The candidate nodules are extracted and pruned based on the rules that are built using characteristics of nodules. The 2D and 3D Geometric features and Intensity Based Statistical features are extracted and used to train a Neural Network. The proposed Computer-Aided Diagnostic (CAD) system is tested and validated using standard dataset of Lung Image Consortium Database (LIDC). The results obtained from proposed CAD system are good as compared to existing CAD systems. The sensitivity of 96.95% is achieved with accuracy of 96.68%.

Digital Object Identifier (DOI)

http://dx.doi.org/10.12785/amis/090124

Recommended Citation

Akram, Sheeraz; Younus Javed, Muhammad; Qamar, Usman; Khanum, Aasia; and Hassan, Ali (2015) "Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images," Applied Mathematics & Information Sciences: Vol. 09: Iss. 1, Article 31.
DOI: http://dx.doi.org/10.12785/amis/090124
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss1/31