Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification

Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. We assemble a retrospective dataset involving 12 cases of mammograms have benign and malignant lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a cross-validation method with various folds of 10, 5 and 3. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated.

Keywords: breast cancer diagnosis, computer-aided diagnosis (CAD) of mammograms, feature dimensionality reduction, lesion classification, random projection algorithm, support vector machine (SVM).