Features based Mammogram Image Classification using Weighted Feature Support Vector Machine

TL;DR

This paper presents an automated mammogram classification method using a Weighted Feature Support Vector Machine that emphasizes relevant features, resulting in improved accuracy and fewer support vectors compared to traditional SVM.

Contribution

It introduces a novel weighted feature kernel for SVM that enhances classification of breast tissue as benign or malignant using combined image and clinical features.

Findings

Texture features improve accuracy more than other features.

WFSVM creates fewer support vectors than standard SVM.

The method achieves better classification performance.

Abstract

In the existing research of mammogram image classification, either clinical data or image features of a specific type is considered along with the supervised classifiers such as Neural Network (NN) and Support Vector Machine (SVM). This paper considers automated classification of breast tissue type as benign or malignant using Weighted Feature Support Vector Machine (WFSVM) through constructing the precomputed kernel function by assigning more weight to relevant features using the principle of maximizing deviations. Initially, MIAS dataset of mammogram images is divided into training and test set, then the preprocessing techniques such as noise removal and background removal are applied to the input images and the Region of Interest (ROI) is identified. The statistical features and texture features are extracted from the ROI and the clinical features are obtained directly from the dataset. The extracted features of the training dataset are used to construct the weighted features and precomputed linear kernel for training the WFSVM, from which the training model file is created. Using this model file the kernel matrix of test samples is classified as benign or malignant. This analysis shows that the texture features have resulted in better accuracy than the other features with WFSVM and SVM. However, the number of support vectors created in WFSVM is less than the SVM classifier.