Breast cancer is a disease where there is excessive growth or uncontrolled growth of cells of the breast tissue. Breast cancer is a type of cancer that is often found as lump, bloody nipple, pain or sore and change in size in the most of the cases. Breast cancer occurs when the cell tissues of the breast become abnormal and uncontrollably divided. These abnormal cells form a large lump of tissues, which consequently becomes a tumor. Digital imaging techniques like Scintimammography are used to analyse metabolic activities and vascular circulation for pre-cancerous analysis based on breast tissue. Finally biopsy result is used to ascertain cancer when other physical exam and mammograms show breast change. Soft computing techniques are interestingly gaining popularity in medical disease diagnosis and decision making. There are different soft computing techniques for medical data processing. These techniques can be used individually or hybridizing more to process medical data yields near accurate results in decision making by medical practioners. This paper reviews different breast cancer diagnosis methodologies which use information obtained from imaging techniques such as Magnetic Resonance Imaging (MRI), Mammogram, Ultrasound and Biopsy. Aim of this paper is to propose or identify methodologies that process cancerous images obtained from different imaging techniques and predict breast cancer with relatively better accuracy. In this paper, we reviewed research articles published in the recent years on breast cancer prediction using soft computing techniques. Comparative analysis of different methods in terms of accuracy, sensitivity, specificity and computational time is presented.

Soft Computing, Machine Learning, Breast Cancer, Disease Diagnosis, Mammograms, MRI, Classification

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