A support vector machine approach for detection of microcalcifications

Abstract

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.

Support Vector Machine Based Educational Resources Classification

“…It is a well-known learning algorithm that has been widely used in many applications including classification, estimation and tracking as in [1]- [3] and [4]. SVM finds the closest data vectors called support vectors (SV), to the decision boundary in the training set and it classifies a given new test vector by using only these closest data vectors [5], [6].…”

“…In this paper, a Support Vector Machine (SVM) [1] based education resources automatic classifier is described to address this issue in education resources classifying for example. This method can be applied to other raw educational data, which contains sematic context expressing the main content or purpose of the data.…”

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

“…Utilizing an SVM, a set of cancer images was first classified by Bazzani et al and their findings have been compared with the Multi Layer Perception (MLP) technique [1]. Naqa et al [2] utilized the kernel method along with SVM techniques for better performance for the classification, where they obtained around 93.20% accuracy. A set of Histopathological images has been classified using Scale Invariant Feature Transform (SIFT) and Discrete Cosine Transform (DCT) features with an SVM for classification by Mhala et al [3].…”

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

“…El Naqa et al [29] improved the detection rate by applying a successive enhancement learning procedure.…”