Background: Breast cancer is one of the most leading cause of cancer death among women. Early detection of cancer increase the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for Breast cancer detection in mammograms, which does not depend on that number of training samples.
Objective: The paper aims to develop a core vector machine based diagnosis system for breast cancer detection using the date from MIAS. The main motivation in using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy.
Methods: The proposed method is done in four stages: 1) Pre-Processing is done to extract the breast region using Global thresholding and enhancement using histogram equalization 2) Identification of potential mass using Otsu thresholding 3) Feature extraction using Laws Texture energy measures 4) Mass detection is done using Core vector machine (CVM) classifier.
Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier has produced a promising result in terms of sensitivity (96.9%), Misclassification rate (0.0443) and Accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms.
Conclusion: Performance analysis shows that CVM classifier is superior to other classifier like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process is also analysed and found to be better than other discussed algorithms. The results achieved shows that CVM classifier is best algorithm for breast mass detection in mammograms.