Decision support detection system for lung nodule abnormalities based on machine learning algorithms
Objective Investigates the possibility of the early detection in the case of lung infection. Most cases of lung cancer are detected in advance stages as this type is hard to be detected in premature phases. The Zero-Change dataset was chosen to measure the systems' performance on nodule growth. The chosen dataset is assumed as a proven clinical dataset and was used by several researchers in their proposed systems. The designed detection technique has been considered to be used as a decision support tool. This technique is based on using two machine learning algorithms for classification purposes.
Methods Machine learning techniques was applied to detect interesting patterns and manipulate the dataset images in order to enhance the classification task. Preprocessing procedures also have been applied using different MATLAB functions. In addition two well-known techniques that related to the SVMs; the RBF kernel based support vector machines and the polynomial kernel based support vector machines have also applied using MATLABÂ© package named PRTools.
Results The performance of this paper proposed technique was evaluated based on several values of both chosen techniques. The procedure was implemented on the basis of leave-one-out-cross-validation procedure in order to generate unbiased outcomes. The results of cross validation procedure is averaged and presented as a classifier outcome. The misclassification error, sensitivity, specificity and accuracy are calculated to show a clear image about the two classifiers.
Conclusion The experimental results have shown that the proposed system has scored high accuracy by Polynomial kernel SVM. A set of distinguishable representative features which are correlated together by a statistics association. Also this designed system can be considered as a benchmark for developing of other tissues abnormalities signs detection systems.
2. Krishnan K, Ibanez L, Turner WD, Jomier J, Avila RS. An open-source toolkit for the volumetric measurement of CT lung lesions. Opt Express. 2010;18:15256â€“15266.
3. Sajda P. Machine learning for detection and diagnosis of disease. Annu. Rev. Biomed. Eng. 2006;8:537â€“565.
4. Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell. 2013; 35:1798â€“1828.
5. Xu Y, Mo T, Feng Q, Zhong P, Lai M, Chang EI. Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Florence, Italy, 2014, pp. 1626â€“1630.
6. Bellotti R, De Carlo F, Gargano G, Tangaro S, Cascio D, Catanzariti E, et al. A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. Med Phys. 2007; 34:4901â€“4910.
7. Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R. Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys. 2011;38:1962â€“1971.
8. Camarlinghi N, Gori I, Retico A, Bellotti R, Bosco P, Cerello P, et al. Combination of computer-aided detection algorithms for automatic lung nodule identification. Int J Comput Assist Radiol Surg. 2012; 7:455â€“464.
9. Abdulla AA, Shaharum SM. Lung cancer cell classification method using artificial neural network. Inform Eng Lett. 2012;2:49â€“59.
10. Kuruvilla J, Gunavathi K. Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed. 2014;113:202â€“209.
11. Moler EJ, Chow ML, Mian IS. Analysis of molecular profile data using generative and discriminative methods. Physiol Genomics. 2000;4:109â€“126.
12. Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci. 2004;44:1936â€“1941.
13. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16:906â€“914.
14. Segal NH, Pavlidis P, Antonescu CR, Maki RG, Noble WS, DeSantis D, et al. Classification and subtype prediction of adult soft tissue sarcoma by functional genomics. Am J Pathol. 2003;163:691â€“700.
15. Segal NH, Pavlidis P, Noble WS, Antonescu CR, Viale A, Wesley UV, et al. Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling. J Clin Oncol. 2003;21:1775â€“1781.
16. Statnikov A, Aliferis CF, Tsamardinos I, Hardin D, Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics. 2005;21:631â€“643.
17. Alsallal M. A Machine Learning Approach for Plagiarism Detection. PhD diss., Coventry University, 2016.
18. Vapnik V. The Nature of Statistical Learning Theory. Springer Science & Business Media, New York, 2013.
19. Aizerman MA, Braverman EM, Rozonoer LI. Theoretical foundations of the potential function method in pattern recognition learning. Automation Remote Contr. 1964;25:821â€“837.
20. Duin RPW, Juszczak P, Paclik P, Pekalska E, de Ridder D, Tax DMJ, et al. PRTools4.1. A Matlab Toolbox for Pattern Recognition, Delft University of Technology.