Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier

TL;DR

This paper presents an automated breast mass detection method using transfer learning with VGG19 for feature extraction, followed by SVM classification, achieving high accuracy on the INbreast dataset.

Contribution

It introduces a novel combination of VGG19 features with SVM classifiers for improved mass detection in mammograms, with extensive experiments validating robustness.

Findings

Achieved an AUC of 0.994 on INbreast dataset.

VGG19 features effectively distinguish between mass and non-mass.

SVM classifiers demonstrate high robustness and accuracy.

Abstract

Mammography is the most widely used gold standard for screening breast cancer, where, mass detection is considered as the prominent step. Detecting mass in the breast is, however, an arduous problem as they usually have large variations between them in terms of shape, size, boundary, and texture. In this literature, the process of mass detection is automated with the use of transfer learning techniques of Deep Convolutional Neural Networks (DCNN). Pre-trained VGG19 network is used to extract features which are then followed by bagged decision tree for features selection and then a Support Vector Machine (SVM) classifier is trained and used for classifying between the mass and non-mass. Area Under ROC Curve (AUC) is chosen as the performance metric, which is then maximized during classifier selection and hyper-parameter tuning. The robustness of the two selected type of classifiers, C-SVM, and \u{psion}-SVM, are investigated with extensive experiments before selecting the best performing classifier. All experiments in this paper were conducted using the INbreast dataset. The best AUC obtained from the experimental results is 0.994 +/- 0.003 i.e. [0.991, 0.997]. Our results conclude that by using pre-trained VGG19 network, high-level distinctive features can be extracted from Mammograms which when used with the proposed SVM classifier is able to robustly distinguish between the mass and non-mass present in breast.