Early Detection of Breast Cancer using SVM Classifier Technique

TL;DR

This paper introduces a mammogram tumor detection system that enhances images, segments tumors, extracts features, and classifies suspicious regions using an SVM classifier, achieving high sensitivity on a standard database.

Contribution

It proposes a comprehensive tumor detection pipeline combining image enhancement, segmentation, feature extraction, and SVM classification for improved breast cancer detection.

Findings

Achieved 88.75% sensitivity on mini-MIAS database.

Effective tumor detection in low-contrast mammograms.

Combines multiple image processing techniques for accurate classification.

Abstract

This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of (a) mammogram enhancement. (b) The segmentation of the tumor area. (c) The extraction of features from the segmented tumor area. (d) The use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.