Lung nodule diagnosis from CT images based on ensemble learning

Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.

[1]  Abbas Z. Kouzani,et al.  Automated identification of lung nodules , 2008, 2008 IEEE 10th Workshop on Multimedia Signal Processing.

[2]  Mislav Grgic,et al.  Automatic CT Image Segmentation of the Lungs with Region Growing Algorithm , 2011 .

[3]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[4]  J. Austin,et al.  Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. , 1996, Radiology.

[5]  Gagan Jindal,et al.  Identifying Lung Cancer Using Image Processing Techniques , 2011 .

[6]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[7]  M. S. Al-Tarawneh.  Lung Cancer Detection Using Image Processing Techniques , 2012 .

[8]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[9]  Noboru Yanai,et al.  Prognosis of non-surgically treated, clinical stage I lung cancer patients in Japan. , 2002, Lung cancer.

[10]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[11]  A. Akan,et al.  A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  K. Gunavathi,et al.  Lung cancer classification using neural networks for CT images , 2014, Comput. Methods Programs Biomed..

[13]  Robin N. Strickland.  Tumor detection in nonstationary backgrounds , 1994, IEEE Trans. Medical Imaging.

[14]  Ehsanollah Kabir,et al.  A subspace approach to error correcting output codes , 2013, Pattern Recognit. Lett..

[15]  Safdar Ali,et al.  Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines , 2014, Comput. Methods Programs Biomed..

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[18]  Abbas Z. Kouzani,et al.  Automated detection of lung nodules in computed tomography images: a review , 2010, Machine Vision and Applications.

[19]  Matthew T. Freedman,et al.  Artificial convolution neural network techniques and applications for lung nodule detection , 1995, IEEE Trans. Medical Imaging.

[20]  M.N. Mughal,et al.  Early lung cancer detection by classifying chest CT images: a survey , 2004, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[21]  E. Hoffman,et al.  Assessment of the pulmonary structure-function relationship and clinical outcomes measures: quantitative volumetric CT of the lung. , 1997, Academic radiology.

[22]  Michael C. Lee,et al.  Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction , 2010, Artif. Intell. Medicine.

[23]  Thomas G. Dietterich.  Machine-Learning Research , 1997, AI Mag..

[24]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  S. S. Singh,et al.  Lung Cancer Detection on CT Images by Using Image Processing , 2012, 2012 International Conference on Computing Sciences.

[26]  Abbas Z. Kouzani,et al.  Random forest based lung nodule classification aided by clustering , 2010, Comput. Medical Imaging Graph..

[27]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[28]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[29]  O. Miettinen,et al.  Early Lung Cancer Action Project: overall design and findings from baseline screening , 1999, The Lancet.

[30]  Amir-Masoud Eftekhari-Moghadam,et al.  Knowledge discovery in medicine: Current issue and future trend , 2014, Expert Syst. Appl..

[31]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[32]  Ilaria Gori,et al.  Combination of computer-aided detection algorithms for automatic lung nodule identification , 2011, International Journal of Computer Assisted Radiology and Surgery.

[33]  A. H. El-Baz,et al.  Hybrid intelligent system-based rough set and ensemble classifier for breast cancer diagnosis , 2014, Neural Computing and Applications.