MAMMOGRAM IMAGES DETECTION USING SUPPORT VECTOR MACHINES

Raja Sekar Mummalaneni ,Dr.n Sandhya

doi:10.26483/ijarcs.v8i7.4290

Abstract

Abstract: Breast cancer begins when an abnormal growth of cells takes place in the breast. We formulated a procedure which explains identification of cancer cells in breast cancer X-ray images. This study is useful for doctors to discover abnormal tissues in given set of X-ray images. The initial phase of this procedure intended to enhance the mammogram image sequence. Initial phase is data cleaning phase in which noise is removed and emphasizing the inner structure of the mammogram image. In the second phase CNNs are used to segment the regions which consists of cancer cells. These regions may have various shapes like circular density, eccentricity, density, circularity and circular disproportion. Shape descriptors are used to asses the shapes of the regions of interest. Textures are analyzed with the help of geostatistic functions like Geary’s index and Moran’s index. SVMs are used to categorize the brain image into two regions such as non-masses and masses, with 0.3 false negative value per mammogram image, 0.86 false positive value per mammogram image, sensitivity 79% and ROC value 90%.

Highlights

LITERATURE SURVEYFrom the past few years, various researchers have been published articles to identify breast cancer textures in mammograms images
Our study offers a computer aided procedure to assist an expert to identify abnormal growth in breast cancer images
In this method we used cellular non linear networks for segmenting mammogram image regions and which are useful to obtain the attributes from mammogram images

Summary

INTRODUCTION

From the past few years, various researchers have been published articles to identify breast cancer textures in mammograms images. Our study offers a computer aided procedure to assist an expert to identify abnormal growth in breast cancer images In this method we used cellular non linear networks for segmenting mammogram image regions and which are useful to obtain the attributes from mammogram images. Oliveira et al [14] formulated a method based on GNG (Growing Neural Gas) to divide the mass sample regions and Ripley’s values associated with support vector machines to identify textures in breast cancer images. They worked extensively on DDSM image databases and obtained remarkable accuracy in classifying breast cancer images. While choosing the mammogram images from DDSM, criteria followed was that each mammogram must have only one mass

Preprocessing

Acquirement Of Mammogram Images

Segmentation

Feature Extraction

RESULTS AND DISCUSSION

CONCLUSION