International Journal of Recent Trends in Engineering & Research

online ISSN

Classification of Mammogram Images By Machine Learning Algorithm

Publication Date : 28/03/2019


DOI : 10.23883/IJRTER.CONF.20190322.054.1ZH1F


Author(s) :

A.Anbumani , M. Meenatchi.


Conference Name :
1st International Conference on New Scientific Creations in Engineering and Technology - 2019



Abstract :

Breast cancer is very common in women's nowadays. It initially starts when cells in the breast begin to grow out of control. These cells usually form a tumor that will often be observed on an x-ray or felt as a lump. Cells in nearly any part of the body can become cancer and can spread to other areas of the body. There are almost 6 stages of breast cancer. It is always found that the detection of cancer at the first stage can cure it. A sample image is taken as an input and compared with the images already stored in database detected with cancer. If the detection is found successful then corresponding treatment is suggested. The stage of cancer is been demonstrated and respective treatment is been advised to the patient. Stage wise treatment and medicines are given to cure that cancer. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy, precision, sensitivity and specificity. In existing method Rule based approach is used for classification which gives static range value for different classes. Therefore we will not able dynamic images or outlier behavior images. Classifiers are not able to distinguish feature overlapping. Therefore at learning phase pattern of image is not identified. In Proposed system we apply Support Vector Machine as a classifier on the mammogram images to enhance the accuracy rate. This approach performs well on overlapping problem. This method is different from all other approaches, which are used to identify normal mammograms by detecting cancers. Overlapped tissues are also detected by this using this classifier. Experimental results show that SVM gives the highest accuracy with lowest error rate.


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