Breast Cancer: Classification of Breast Masses Mammograms using Artificial Neural Network and Support Vector Machine
This paper presents the diagnosis of breast cancer by using ANN and SVM. To deal with the different kinds of abnormalities causing Cancer, this report consists of all the modalities which help in detecting cancer and well as different methods of feature extraction. Such modalities can be named as: Mammography, Ultrasound, MRI etc [1]. Currently, Electrical impedance and nuclear medicine are used widely for diagnosis. These modalities Based on the image processing i.e. identification of abnormality is done through the reading and retrieving information from images. But this research is based on mammogram images. Before retrieving information one should know about all kinds of abnormalities like: micro classification, masses, architectural distortion, asymmetry, and breast density etc.[2]. And after the process of extracting the abnormal part or can say that ROI (Region of Interest) on which the treatment is applied. To extract ROI various methods are used like region growing, edge detection, segmentation etc. [3][4]. Then, feature extraction is done from which a lot of features are extracted on which feature selection is applied to get higher accuracy. After going through all researches done till now here I have got the conclusion that for determining the presence of cancer researcher uses different features but till now only few researcher used two features named shape and texture which needs good classification technique[1]. Then, classify into classes of normal and abnormal classes. From the statistical study it has been found that the trend in increasing cancer every year, thus, the best and most effective way to cure cancer is the removal of cancerous part.
Published by: Kamaldeep Kaur, Er. Pooja
Author: Kamaldeep Kaur
Paper ID: V2I3-1200
Paper Status: published
Published: June 29, 2016
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