• S. Piramu Kailasam Department of Computer Science, Sadakathullah Appa College (Affiliated to Manonmaniam Sundaranar University, Tirunelveli), Palayamkottai Tirunelveli, Tamilnadu, India
  • E. Siva Sankari Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India
  • R. Kumuthini Department of Physics, Sadakathullah Appa College (Affiliated to Manonmaniam Sundaranar University, Tirunelveli), Palayamkottai Tirunelveli, Tamilnadu, India



CT Images, Descriptors, Classifiers, ROC, CNN, features


Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine learning in an era of big data is that significant hierarchical relationships within the data can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) is used as feature descriptors in pulmonary malignancy prediction. Various feature descriptors such as Histogram of Oriented Gradient (HOG), Extended Histogram of Oriented Gradient (EXHOG) and Linear Binary Pattern (LBP) descriptors are analyzed with classifiers such as Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for Computed Tomography (CT) The phenotype features of pulmonary nodules are important cues for identification. The nodule solidity is an important cue for white blob area identification. The method is analyzed in Lung Image Database Consortium (LIDC) dataset. Receivers Operating Characteristics (ROC) curves show the graphical summaries of detectors performance. It is proved that CNN based feature extraction with SVM classifier works well in pulmonary malignancy prediction.


Abdulrazzaq, M. M., S. A. Noah, and M. A. Fadhil. 2015. X-Ray Medical Image Classification Based on Multi Classifiers. 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT). 218-223. doi: 10.1109/ACSAT.2015.45.

Setio, A.A.A., F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. J. Van Riel, M. M. W. Wille, M. Naqibullah, C. I. Sánchez, and B. Van Ginneken. 2016. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging. 35(5): 1160-1169. doi: 10.1109/TMI.2016.2536809.

Armato III, S.G., G. McLennan, L. Bidaut, M.F. McNitt‐Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D.R. Aberle, C. I. Henschke, E.A. Hoffman, and E.A. Kazerooni. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical physics. 38(2): 915-931. doi: 10.1118/1.3528204. PMID: 21452728; PMCID: PMC3041807.

Niranjana, G., and M. Ponnavaikko. 2017. A Review on Image Processing Methods in Detecting Lung Cancer Using CT Images. 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC). 18-25. doi: 10.1109/ICTACC.2017.16.

Chen, S., J. Qin, X. Ji, B. Lei, T. Wang, D. Ni, and J. Z. Cheng. 2016. Automatic scoring of multiple semantic attributes with multi-task feature leverage: a study on pulmonary nodules in CT images. IEEE transactions on medical imaging. 36(3): 802-814. doi: 10.1109/TMI.2016.2629462.

Gould, M.K., J. Donington, W. R. Lynch, P.J. Mazzone, D. E. Midthun, D. P. Naidich, and R. S. Wiener. 2013. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 143(5 Suppl):e93S-e120S. doi: 10.1378/chest.12-2351.

Wang, H., X. H. Guo, Z. W. Jia, H. K. Li, Z. G. Liang, K. C. Li, and Q. He. 2010. Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. Eur J Radiol. 74(1): 124-9. doi: 10.1016/j.ejrad.2009.01.024.

Naidich, D. P, A. A. Bankier, H. MacMahon, Schaefer-Prokop CM, Pistolesi M, J. M. Goo, P. Macchiarini, J. D. Crapo, C. J. Herold, J. H. Austin, and W. D. Travis. 2013. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 266(1): 304-17. doi: 10.1148/radiol.12120628.

Gonçalves, L., J. Novo, and A. Campilho. 2016. Hessian based approaches for 3D lung nodule segmentation. Expert Systems with Applications. 61: 1-15.

Bhuvaneswari, P., and A. Brintha Therese. 2015. Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm. Procedia Materials Science. 10: 433-440.

Sowmyayani, S., 2022. Rapid Study on Feature Extraction and Classification Models in Healthcare Applications. International Journal of Computer and Systems Engineering. 16(12): 620-627.

Nurtiyasari, D., D. Rosadi, and Abdurakhman. 2017. The application of Wavelet Recurrent Neural Network for lung cancer classification. 2017 3rd International Conference on Science and Technology - Computer (ICST), 127-130. doi: 10.1109/ICSTC.2017.8011865.

Epifanio, I., and G. Ayala. 2002. A random set view of texture classification. IEEE Transactions on Image Processing. 11(8): 859-867. doi: 10.1109/TIP.2002.801119.

Soille, P. 2004. Texture Analysis. In: Morphological Image Analysis. Springer. Berlin, Heidelberg. DOI: 10.1007/978-3-662-05088-0-11.

Ciompi, F, C. Jacobs, E.T. Scholten, M.M. Wille, P. A. de Jong, M. Prokop, and B. van Ginneken. 2015. Bag-of-frequencies: a descriptor of pulmonary nodules in computed tomography images. IEEE Trans Med Imaging. 34(4): 962-73. doi: 10.1109/TMI.2014.2371821.

Amirani, M.C., Zahra Sadeghi Gol, and Ali Asghar Beheshti Shirazi. 2008. Efficient Feature Extraction for Shape-Based Image Retrieval. Journal of Applied Sciences. 8: 2378-2386. DOI: 10.3923/jas.2008.

Rao, P., N. A. Pereira, and R. Srinivasan. 2016. Convolutional neural networks for lung cancer screening in computed tomography (CT) scans. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). 489-493. doi: 10.1109/IC3I.2016.7918014.

Song, Q., Lei Zhao, XingKe Luo, and XueChen Dou. 2017. Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. Journal of Healthcare Engineering. DOI: 10.1155/2017/831470.

Dhaware, B. U., and A. C. Pise. 2016. Lung cancer detection using Bayasein classifier and FCM segmentation. 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). 170-174. doi: 10.1109/ICACDOT.2016.7877572.

Sammouda, R. 2016. Segmentation and Analysis of CT Chest Images for Early Lung Cancer Detection. 2016 Global Summit on Computer & Information Technology (GSCIT). 120-126, doi: 10.1109/GSCIT.2016.29.

Lecun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE. 86(11): 2278-2324. doi: 10.1109/5.726791.

Dhara, A. K, S. Mukhopadhyay, A. Dutta, M. Garg, and N. Khandelwal. 2016. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images. J Digit Imaging. 29(4): 466-75. doi: 10.1007/s10278-015-9857-6.

Quratul, A., Jaffar, Arfan Choi, and Tae-Sun. 2014. Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor. Applied Soft Computing. 21: 330–340. Doi: 10.1016/j.asoc.2014.03.019.

Beijbom, O., P. J. Edmunds, D. I. Kline, B. G. Mitchell, and D. Kriegman. 2012. Automated annotation of coral reef survey images. IEEE Conference on Computer Vision and Pattern Recognition. 1170-1177. doi: 10.1109/CVPR.2012.6247798.

Pun, C. M., Zhu, and Hong-Min. 2009. Textural image segmentation using discrete cosine transform. Proceedings of the 3rd International Conference on Communications and Information Technology. pp. 54-58.

Goharian, and Grossman. 2003. Data Mining Classification, Illinois Institute of Technology. Chidanand Apté, Sholom Weiss, Data mining with decision trees and decision rules. Future Generation Computer Systems. 13(2–3): 197-210,

Satpathy, A., X. Jiang, and H-L. Eng. 2011. Extended Histogram of Gradients with Asymmetric Principal Component and Discriminant Analyses for Human Detection. Proceedings - 2011 Canadian Conference on Computer and Robot Vision, CRV. 64-71. 10.1109/CRV.2011.16.

Sathik, M.M., and S. P. Kailasam. Pulmonary CT Image analysis for Nodule Detection using Inspired FCM Clustering.

Kailasam, S.P., and M.M. Sathik. 2019. A novel hybrid feature extraction model for classification on pulmonary nodules. Asian Pacific Journal of Cancer Prevention. 20(2): 457.

Lu, D., and Q. Weng. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 28(5): 823 870. DOI: 10.1080/01431160600746456

Naseem, R., K. S. Alimgeer, and T. Bashir. 2017. Recent trends in Computer Aided diagnosis of lung nodules in thorax CT scans. 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT). 1-12, doi: 10.1109/ICIEECT.2017.7916548.

Sherly Alphonse A., and Dejey Dharma. 2018. Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition. Multimedia Tools Appl. 77(8): 9455–9488.

Gopi, K., and J. Selvakumar. 2017. Lung tumor area recognition and classification using EK-mean clustering and SVM. 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). pp. 97-100. doi: 10.1109/ICNETS2.2017.8067906.

Sankari, E.S., and D. Manimegalai. 2017. Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets. J Theor Biol. 21(435): 208-217. doi: 10.1016/j.jtbi.2017.09.018




How to Cite

Kailasam, S. P. ., Sankari, E. S. ., & Kumuthini, R. . (2023). ROC CURVE ANALYSIS OF DIFFERENT HYBRID FEATURE DESCRIPTORS USING MULTI CLASSIFIERS . ASEAN Engineering Journal, 13(2), 53-60.