Automatic Diagnose of the Stages of Breast Cancer using Intelligent Technique

The objective of this paper is to locate the stage of the breast cancer and to locate the tumor tissues. The proposed work is performed in two stages. In the first stage, location of tumour is done and in the second phase retrieving of the tumor images and its stages based on the “query input”. Texture and Shape features are used here as Feature descriptors. Shape features used are asymmetry, aspect ratio, eccentricity and Bending Energy. Texture features used are contrast, energy and Gabor Filter. KNN classifier is used for classification and for Pattern matching, Euclidean distance is used. The proposed approach which gives 95.6% accuracy has been compared with earlier approaches. The proposed approach was tested with 32 samples and got annotated by radiologists.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[3]  G. Wiselin Jiji,et al.  Diagnose the Stages of Breast Cancer using SVM , 2012 .

[4]  H. Honda,et al.  Detection of breast cancer by soft-copy reading of digital mammograms: Comparison between a routine image-processing parameter and high-contrast parameters , 2010, Acta radiologica.

[5]  Bipin C. Desai,et al.  A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback , 2007, IEEE Transactions on Information Technology in Biomedicine.

[6]  A. Chan,et al.  An artificial intelligent algorithm for tumor detection in screening mammogram , 2001, IEEE Transactions on Medical Imaging.

[7]  M. Emre Celebi,et al.  A comparative study of three moment-based shape descriptors , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[8]  M. El-Shenawee,et al.  Broadband Dual Linear Polarized Antenna for Statistical Detection of Breast Cancer , 2008, IEEE Transactions on Antennas and Propagation.

[9]  Yannis Theodoridis,et al.  A Unified and Flexible Framework for Comparing Simple and Complex Patterns , 2004, PKDD.

[10]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[11]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.