A REVIEW ON BREAST CANCER DETECTION USING ARTIFICIAL NEURAL NETWORK

B. Ramya

Abstract


The Breast Cancer disease is curable if detected in early stage. Screening is carried out on the basis of mammogram; this is used in x-ray image to reveal lumps in the breast. Calcium deposit can also indicate the existence of a tumor in breast. The breast Tumor are of two types first is Benign and second is Malignant, Benign Tumor which is non-cancerous and not life threatening and Malignant Tumor are cancerous and life threatening. Mammography is proven as efficient tool to detect breast cancer before clinical symptoms appears digital mammography is currently considered as standard procedure for breast cancer diagnosis, various artificial intelligence techniques are used for classification problems in the area of medical diagnosis.  This paper presents a research on breast cases of 68 samples proven breast tumor are analyzed and classified into benign and malignant categories using ANN. Two different feature extraction methods GLCM and Intensity based features are explained here.


Keywords


Artificial Neural Network (ANN), GLCM, Intensity based features, Digital Mammograms, Wavelet transform.

Full Text:

PDF

References


Pradeep N, Girlish H & et.al./”Feature extraction of mammogram”, International Journal of Bioinformatics research 2012, vol.4 pp-214- 244.

Shekhar Singh, Dr P R Gupta/”breast cancer detection & Classification by NN”vol no.6.

R.Nithya,B.Santhi/”comparative study of feature extraction, method for breast cancer classification”/Journal of theoretical and applied Information Technology, 30.11.2011, vol.33 no.2.pp 220-224.

Tabar L. and Dean P.B.(2003)/”Composition &methods for detection and treatment of breast cancer”.GynaecolObstet,82, pp319- 326.

M.Vasantha et.al./”Medical Image feature extraction selection and classification”/International Journal of engineering science and technology vol.2 (6), 2010,pp. 2071-2076.

A.MohdKhuzi, R.Besar & et.al.”Identification of masses in digital mammogram using gray level co-occurrences matrices”/Biomedical Imaging and Intervention Journal, 2009.

Kulkarni D, Bhagyashree S and Udupi G(2010) Texture Analysis of mammographic images.int.j.com.appl.5,12_17.

Dubey R, Hanumandla M & Gupta S(2010) level set detected masses in digital ammograms. Indian.j.sci.Technol.3(1):9-13.

Singh N and Mohapatra A (2011)Breast cancer mass detection inmammograms using K-means clustering .int.j.com.appl.22(2),15-21.

Usha Rani K(2010) parallel approach for diagnosis of breast cancer using NN, int.j.com.appl.(09758887),10(3), 1-5.

Ms Tripty Singh, Dr. Sarita Singh Bhadauoria, Dr A.K Wadwani and Dr S.Wadhwani, “Contrast Enhancement of Clusters In Images Using Fuzzy-Rule Based Algorithm”, International Journal of Advances in Scienceand Technology, Feb Issue Vol. 2, No. 2, pp. 18-28, 2011.

Ms Tripty Singh, Dr. Sarita Singh Bhadauoria, Dr. A.K Wadwani and Dr. S.Wadhwani, “Wavelet Based Contrast Enhancement and Adaptive Noise Cancellation in Mammograms”, International Journal of Science and Advances Technology, Vol. 1, Issue. 9, ,pp. 256-263, Nov 2011.

Ms Tripty Singh, Dr. Sarita Singh Bhadauoria, Dr. A.K Wadwani and Dr. S.Wadhwani, “Computer Aided Diagnosis System for Breast Cancer”, International Journal of Scientific and Research Publications, Vol. 1, Issue. 2, , pp. 1-5 January-2012.


Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2020 INTERNATIONAL EDUCATION AND RESEARCH JOURNAL