Diagnosis of Breast Cancer Using Morphological Filters and Classification Using Neural Networks

Mammography is the best method for the early diagnosis of breast cancer. This paper provides an automatic way for the classification of cancer. The image which may be prone to noise is denoised for accurate diagnosis. To have a better outlook at the image it is enhanced using non linear operator function. The tumour which is to be segmented for further investigation is done morphological filters. Features that are extracted from the segmented image Gray Level Co occurrence Matrix (GLCM) is fed to the Back Propagation Network (BPN) for the classification of the tumour. The main idea behind this paper is to reduce the error of the classification and to give accurate results.

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