Fusion of handcrafted and deep transfer learning features to improve performance of breast lesion classification

Abstract

Computer-aided detection and/or diagnosis schemes typically include machine learning classifiers trained using either handcrafted features or deep learning model-generated automated features. The objective of this study is to investigate a new method to effectively select optimal feature vectors from an extremely large automated feature pool and the feasibility of improving the performance of a machine learning classifier trained using the fused handcrafted and automated feature sets. We assembled a retrospective image dataset involving 1,535 mammograms in which 740 and 795 images depict malignant and benign lesions, respectively. For each image, a region of interest (ROI) around the center of the lesion is extracted. First, 40 handcrafted features are computed. Two automated feature set are extracted from a VGG16 network pretrained using the ImageNet dataset. The first automated feature set is extracted using pseudo color images created by stacking the original image, a bilateral filtered image, and a histogram equalized image. The second automated feature set is created by stacking the original image in three channels. Two fused feature sets are then created by fusing the handcrafted feature set with each automated feature set, respectively. Five linear support vector machines are then trained using a 10- fold cross-validation method. The classification accuracy and AUC of the SVMs trained using the fused feature sets performs significantly better than using handcrafted or automated features alone (p<0.05). Study results demonstrate that handcrafted and automated features contain complimentary information so that fusion together create classifiers with improved performance in classifying breast lesions as malignant or benign.