Comparison of performance in breast lesions classification using radiomics and deep transfer learning: An assessment study

Radiomics and deep transfer learning have been attracting broad research interest in developing and optimizing CAD schemes of medical images. However, these two technologies are typically applied in different studies using different image datasets. Advantages or potential limitations of applying these two technologies in CAD applications have not been well investigated. This study aims to compare and assess these two technologies in classifying breast lesions. A retrospective dataset including 2,778 digital mammograms is assembled in which 1,452 images depict malignant lesions and 1,326 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. First, one scheme is applied to segment lesions and compute radiomics features, while another scheme applies a pre-trained residual net architecture (ResNet50) as a transfer learning model to extract automated features. Next, the same principal component algorithm (PCA) is used to process both initially computed radiomics and automated features to create optimal feature vectors by eliminating redundant features. Then, several support vector machine (SVM)-based classifiers are built using the optimized radiomics or automated features. Each SVM model is trained and tested using a 10-fold cross-validation method. Classification performance is evaluated using area under ROC curve (AUC). Two SVMs trained using radiomics and automated features yield AUC of 0.77 +/- 0.02 and 0.85 +/- 0.02, respectively. In addition, SVM trained using the fused radiomics and automated features does not yield significantly higher AUC. This study indicates that (1) using deep transfer learning yields higher classification performance, and (2) radiomics and automated features contain highly correlated information in lesion classification.