A Comparative Study to Evaluate the Performance of Classification Algorithms in Mammogram Analysis

  • Abstract
  • Keywords
  • References
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  • Abstract

    Breast cancer is a major threat humans are facing irrespective of geographical limits. The awareness about breast cancer has increased during the last decade and many preventive measures were in practice to detect the breast cancer before the symptoms were felt. Mammography is a screening methodology currently in practice. In this paper the mammogram image is analyzed using automated system. The automated system is designed to be capable of distinguishing the mammogram image into a normal or malignant. This process involves image enhancement and image segmentation at preprocessing level. Histogram equalization technique is used to transform low contrast region of the mammogram into region with higher contrast and Fuzzy C Means (FCM) algorithm is used to segment the mammogram image into regions suitable for further analysis. After enhancement and segmentation at preprocessing level the classification is done using three classification algorithms like decision tree classifier, Neural Network classifier and Support Vector Machine (SVM). The performance of the classification algorithms is evaluated using the following criteria like speed, flexibility, robustness, scalability, interpretability, Time complexity and also based on accuracy, sensitivity and specificity. The results obtained in classification are compared with other classification algorithms. It is found that the neural network classifier approach produces better results compared to other classifiers.The average accuracy in diagnosis by Neural Network approach classifier is around 91%.  Also it is found that the decision tree approach is much flexible and easy to use compared to other approaches.



  • Keywords

    Image enhancement, mammogram, automated systems , benign, malignant, histogram equalization, robustness.

  • References

      [1] www.cancer.org/breast-cancer-early-detection

      [2] www.cancer.org/cancer/breastcancer/.../breastcancerearlydetection/breast

      [3] www.cancer.gov/cancertopics

      [4] Gonzalez RC, Woods RE & Eddins SL, Digital Image Processing Using MATLAB,2nd Edition, McGraw Hill Education (India) Private Limited, (2013).

      [5] http://www.breastcancerindia.net/bc/statistics

      [6] Gonzalez RC & Woods RE, Digital Image Processing, Third Edition, Pearson Education, Inc, (2009).

      [7] Cortes C & Vapnik V, “Support-vector networks”, Machine learning, Vol.20, No.3, (1995), pp.273–297.

      [8] Sonka M, Hlavac V & Boyle R, “Digital Image Processing and Computer Vision”, Cengage Learning, (2011).

      [9] Chanda B & Dutta Majumder D, Digital Image Processing and Analysis, Prentice Hall of India Private Limited, (2007).

      [10] Shinghal R, Pattern Recognition Techniques and Applications, Oxford University Press, (2008).

      [11] Joshi MA, Digital Image Processing An Algorithmic Approach, PHI Learning Private Limited, (2009).

      [12] www.mias.org

      [13] www.birads.at

      [14] Eddaoudi F, Regragui F, Mahmoudi A & Lamouri N, “Masses Detection Using SVM Classifier Based on Textures Analysis”, Applied Mathematical Sciences, Vol.5, No.8, (2011), pp.367-379.

      [15] Ema T, Doi K, Nishikawa RM, Jiang Y & Papaioannou J, “Image feature analysis and computer-aided diagnosis in mammography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis”, Med. Phys., Vol.22, No.2, (1995), pp.161–169.

      [16] Enderwich CY & Tzanakou EM, “Classification of Mammographic tissue using shape and texture features”, Proceedings of the 19th International Conference-IEEE/EMBS, (1997), pp.810–813.

      [17] Eltoukhy MM, Faye I & Samir BB, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram”, Computers in Biology and Medicine, Vol.40, (2010), pp.384–391.

      [18] Gupta GK, Introduction to Data Mining with Case Studies, Second Edition, PHI Private Ltd, (2011).

      [19] Saravanan K & Sasithra S, “Review on classification based on artificial neural networks”, International Journal of Ambient Systems and Applications (IJASA), Vol.2, No.4,(2014), pp.11-18.




Article ID: 14960
DOI: 10.14419/ijet.v7i3.6.14960

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