ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques
As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes the diagnosis process more challenging, in which the most of the cells are superimposed. By adopting the efficient image processing techniques, the diagnosis process can be made effective, earlier and accurate, where the time aspect is extremely decisive. With those considerations, the main objective of this work is to propose a region based Fuzzy C-Means Clustering (FCM) technique for segmenting the lung cancer region and the Support Vector Machine (SVM) based classification for diagnosing the cancer stage, which helps in clinical practice in significant way to increase the morality rate. Moreover, the proposed ECM-CSD (Efficient Classification Model for Cancer Stage Diagnosis) uses Computed Tomography (CT) lung images for processing, since it poses higher imaging sensitivity, resolution with good isotopic acquisition in lung nodule identification. With those images, the pre-processing has been made with Gaussian Filter for smoothing and Gabor Filter for enhancement. Following, based on the extracted image features, the effective segmentation of lung nodules is performed using the FCM based clustering. And, the stages of cancer are identified based on the SVM classification technique. Further, the model is analyzed with MATLAB tool by incorporating the LIDC-IDRI lung CT images clinical dataset. The comparative experiments show the efficiency of the proposed model in terms of the performance evaluation factors like increased accuracy and reduced error rate.
KeywordsCT lung image ECM-CSD (efficient classification model for cancer stage diagnosis) Fuzzy C-means clustering (FCM) Support vector machine (SVM) Segmentation
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interests and the paper has not beensubmitted elsewhere.
Research involving human participants and/or animals
This article does not contain any studies with human participants or animals performed by any of the authors.
The work does not involve any human or animal participants. The datasets used in the work are taken from free online sources.
- 1.Levner, I., Zhangm, H., Classification driven watershed segmentation. IEEE Transactions on Image Processing. 16:(5), 2007Google Scholar
- 3.Gajdhane, V. A., and Deshpande, L. M., Detection of lung cancer stages on CT scan images by using various image processing techniques. IOSR Journal of Computer Engineering (IOSR-JCE). 16(5): III, 2014. e-ISSN: 2278–0661, p-ISSN: 2278–8727Google Scholar
- 6.Kim, H., Mori, S., Itai, Y., Ishikawa, S., Yamamoto, A., and Nakamura, K., Automatic detection of ground-glass opacity shadows by three characteristics on MDCT images. World congress on Medical Physics and Biomedical Engineering, IFMBE Pro2 Vol. 14(4), 2007.Google Scholar
- 8.Penedo, M.G., Carreira, M.J., Mosquera, A. and Cabello, D., Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Transactions on Medical Imaging. 872–880, 1998.Google Scholar
- 11.Song, Y., Cai, W., Kim, J., Feng, D. D., A Multistage Discriminative Model for Tumor and Lymph Node Detection in Thoracic Images. IEEE Transactions on Medical Imaging. 31(5), 2012.Google Scholar
- 13.ShaikParveen, S., and Kavitha, C., A Review on Computer Aided Detection and Diagnosis of lung cancer nodules. International Journal of Computers & Technology. 3(3), 2012Google Scholar
- 14.Shaik, P. S., and Kavitha, C., Detection of lung cancer nodules using automatic region growing method“, International Conference on Computing, Communications and Networking Technologies IEEE – ICCCNT Digital Object Identifier, 2013. 10.1109/ICCCNT.2013.6726669.Google Scholar
- 20.Wang, L., Support vector machines: theory and applications (Vol. 177). Springer Science & Business Media, 2005.Google Scholar
- 21.Tsochantaridis, I., Hofmann, T., Joachims, T. and Altun, Y., Support vector machine learning for interdependent and structured output spaces. In Proceedings of the twenty-first international conference on Machine learning (p. 104). ACM, 2004.Google Scholar