Classification of Cervical Cancer from MRI Images using Multiclass SVM Classifier

  • Authors

    • Bethanney Janney.J
    • Umashankar G
    • Sindu Divakaran
    • Shelcy Mary Jo
    • Nancy Basilica.S
    2018-05-03
    https://doi.org/10.14419/ijet.v7i2.25.12351
  • Cervical Cancer, MRI Images, Multiclass SVM Classifier, Region growing segmentation, Texture features

  • Cervical Cancer is the abnormal growth of tissues in the lower, narrow part of the uterus (womb) called the Cervix which connects the main body of the uterus, to the vagina or birth canal. Cervical cancer is one of the most common types of cancer that can be seen in women worldwide. Early detection and proper diagnosis can prevent the severity level and reduce the death rates .In this paper, we have proposed an automated diagnosis system of cervical cancer using texture features and Multiclass SVM (Support Vector Machine) Classifier in MRI images. Initially the MRI images are pre-processed to remove undesirable noises and other effects. After pre-processing, the image is segmented by Region growing method to obtain the region of interest. Texture features are extracted from the segmented region. Almost 22 features are extracted at the region of a segmented area and then passed on to Multiclass SVM Classifier to detect if the given image is cancerous or not. The results of the proposed techniques provide effective results for classifying cancerous and the non-cancerous image.

     

  • References

    1. [1] Aswathy.S, M. A. Quereshi, B. Kurian, and K. Leelamoni, “Cervical cancer screening: current knowledge & practice among women in a rural population of Kerala, Indiaâ€, Indian Journal of Medical Research, Vol. 136, No. 2, (2012), pp. 205–210.

      [2] Yessi Jusman, Siew Cheok Ng, and Noor Azuan Abu Osman," Intelligent Screening Systems for Cervical Cancer", The Scientific World Journal, Vol. 2014, (2014), pp. 1-15.

      [3] Wen Wu and Hao Zhou,"Data-Driven Diagnosis of Cervical Cancer With Support Vector Machine-Based Approaches", IEEE Access-Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems ,Vol. 5, (2017), pp. 25189 - 25195.

      [4] Athinarayanan.S, M.V.Srinath,"Automatic Detection and Classification of Cervical Cancer in Pap Smear Images using ETCM & CFE methods based Texture features and Various Classification Techniques", International Journal of Engineering and Computer Science, Vol.5, No.7 ,(2016), pp. 17368-17387.

      [5] Abid Sarwar,"Artificial Intelligence Based Semi-automated Screening of Cervical Cancer using a Primary Training database", Journal of HPV and Cervical Cancer, Vol.1, No.1, (2016), pp.1-10, 2016.

      [6] Pradeep Chandran.K, Smt.U.V.Ratna Kumari, "Improving Cervical Cancer Classification On MR images Using Texture Analysis And Probabilistic Neural Network", International Journal of Science, Engineering and Technology Research (IJSETR),Vol.4, No.9, (2015), pp. 3141-3145.

      [7] Soumya.M.K, K. Sneha and C. Arunvinodh,"Cervical Cancer Detection and Classification Using Texture Analysis", Biomedical & Pharmacology Journal, Vol. 9, No.2, (2016), pp. 663-671.

      [8] Wang X, Wang H, Yang J, Zhang Y, "A New Method for Nonlocal Means Image Denoising Using Multiple Images", PLoS ONE, Vol. 11, No.7, ( 2016), http:// doi.org/10.1371/journal.pone.0158664.

      [9] He N, Lu K,"Images denoising by improved Non-local means algorithm", Communications in Computer and Information Science, Springer, Vol.164, (2011), pp.33-39.

      [10] Prabin, A. and Veerappan. J, "Automatic segmentation of lung ct images by cc based region growing", Journal of Theoretical & Applied Information Technology, Vol. 68, No.1,(2014), pp. 63-69.

      [11] Bethanney Janney J and Emalda Roslin S, "Classification and Detection of Skin Cancer using Hybrid Texture Features, Biomedicine,Vol. 37, No.2, (2017), pp.214-22.

      [12] Dhumal.S, Agrawal.S,"MRI Classification and Segmentation of Cervical Cancer to Find the Area of Tumor", International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol. 3, No.7 ,(2015), pp. 21-26.

      [13] Edwin Jayasingh Mariarputham and Allwin Stephen, "Nominated Texture Based Cervical Cancer Classification", Computational and Mathematical Methods in Medicine, Vol. 2015,(2015) pp. 1-10.

      [14] Sukumar.P, and R. K. Gnanamurthy, “Computer Aided Detection of Cervical Cancer Using Pap Smear Images Based on Adaptive Neuro Fuzzy Inference System Classifierâ€, Journal Of Medical Imaging And Health Informatics, Vol. 6, No. 2, (2016), pp.312–319.

      [15] Turid Torheim, Eirik Malinen, Knut Kvaal, Heidi Lyng, Ulf G. Indahl, Erlend K. F. Andersen, and Cecilia M. Futsæther,"Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines", IEEE Transactions on Medical Imaging, Vol. 33, No.8, (2014), pp.1648-1656.

      [16] Ebtihal Almansour and Arfan Jaffar M, "Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture", .International Journal Compuert Science Network Secuityr, Vol.16, No.4, (2016), pp.135-139.

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  • How to Cite

    Janney.J, B., G, U., Divakaran, S., Mary Jo, S., & Basilica.S, N. (2018). Classification of Cervical Cancer from MRI Images using Multiclass SVM Classifier. International Journal of Engineering & Technology, 7(2.25), 1-5. https://doi.org/10.14419/ijet.v7i2.25.12351