Performance Evaluation of Optimized Artificial Neural Network Classifier for Mammography

  • Authors

    • Amit Sehgal
    • Satish Saini
    • Ritu Vijay
    2018-12-13
    https://doi.org/10.14419/ijet.v7i4.39.24108
  • Mammogram, Computer Aided Diagnosis, Artificial Neural Network

  • This paper works on the detection of the breast cancer at initial stage, by utilizing the mammogram images. The contrast of the mammogram image has been enhanced by pre-processing using histogram equalization. The extracted grey level co-occurrence matrix (GLCM) features have been reduced to the significant subset of features. Then, an ANN classifier has been used to classify the image as malignant or benign. The improvement in sensitivity, specificity, accuracy and f-measure signifies effectiveness of the work.

     

  • References

    1. [1] http://www.cancer.org(2015). American Cancer Society.

      [2] Sherring, Varsha. (2009). Mediating Breast cancer in India. NCA 94th Annual Convention, San Diego.

      [3] Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications, 36(2), 3240-3247.

      [4] West, D., Mangiameli, P., Rampal, R., & West, V. (2005). Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application. European Journal of Operational Research, 162(2), 532-551.

      [5] Azar FS. (2000). Imaging techniques for detecting breast cancer: survey and perspectives. Technical Reports, University of Pennsylvania,1-7.

      [6] Technical Reports (CIS) Department of Computer & Information Science

      [7] Tang J. (2009). Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Transactions on Information Technology in Biomedicine. 13(2), 236-251.

      [8] Wang TC, Karayiannis NB. (1998). Detection of microcalcifications in digital mammograms using wavelets. IEEE transactions on medical imaging, 17(4), 498-509.

      [9] Morton MJ, Whaley DH, Brandt KR, Amrami KK. (2006). Screening Mammograms: Interpretation with Computer-aided Detection—Prospective Evaluation 1. Radiology, 239(2), 375-383.

      [10] Brem RF, Baum J, Lechner M, Kaplan S, Souders S, Naul LG, Hoffmeister J. (2003). Improvement in sensitivity of screening mammography with computer-aided detection: a multi-institutional trial. American Journal of Roentgenology, 181(3), 687-93.

      [11] Deshmukh M, Bhosle U. A survey of image registration (2011). International Journal of Image Processing, 5(3), 245-69.

      [12] Bozek J, Mustra M, Delac K, Grgic M. (2009). A survey of image processing algorithms in digital mammography. Recent advances in multimedia signal processing and Communications, 631-657.

      [13] Keivanfard F, Teshnehlab M, Shoorehdeli MA, Nie K, Su MY. (2010). Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks. 17th IEEE. Iranian Conference of Biomedical Engineering (ICBME),1-4.

      [14] Giger ML, Huo Z. (1999). Artificial neural networks in breast cancer diagnosis: merging of computer-extracted features from breast images. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 99) 3.

      [15] Liu L, Deng M. (2010). An evolutionary artificial neural network approach for breast cancer diagnosis. Third IEEE .International Conference on Knowledge Discovery and Data Mining, 593-596.

      [16] George LE, Mohammed EZ. (2011). Cancer tissues recognition system using box counting method and artificial neural network. IEEE International Conference of Soft Computing and Pattern Recognition (SoCPaR), 5-9.

      [17] Lo JY, Floyd CE. (1999). Application of artificial neural networks for diagnosis of breast cancer. CEC 99. Proceedings of the IEEE Congress on Evolutionary Computation, 3.

      [18] Gonzalez RC, Woods RE. (2008). Digital image processing. Nueva Jersey.

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  • How to Cite

    Sehgal, A., Saini, S., & Vijay, R. (2018). Performance Evaluation of Optimized Artificial Neural Network Classifier for Mammography. International Journal of Engineering & Technology, 7(4.39), 396-400. https://doi.org/10.14419/ijet.v7i4.39.24108