Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging

Breast cancer is the most prevalent cancer and the second cause of cancer related death among women in the United States. Accurate and early detection of breast cancer can reduce the number of mortalities. Recent works explore deep learning techniques with ultrasound for detecting malignant breast lesions. However, the lack of explanatory features, need for segmentation, and high computational complexity limit their applicability in this detection. Therefore, we propose a novel ultrasound-based breast lesion classification framework that utilizes two-dimensional variational mode decomposition (2D-VMD) which provides self-explanatory features for guiding a convolutional neural network (CNN) with mixed pooling and attention mechanisms. The visual inspection of these features demonstrates their explainability in terms of discriminative lesion-specific boundary and texture in the decomposed modes of benign and malignant images, which further guide the deep learning network for enhanced classification. The proposed framework can classify the lesions with accuracies of 98% and 93% in two public breast ultrasound datasets and 89% in an in-house dataset without having to segment the lesions unlike existing techniques, along with an optimal trade-off between the sensitivity and specificity. 2D-VMD improves the areas under the receiver operating characteristics and precision-recall curves by 5% and 10% respectively. The proposed method achieves relative improvement of 14.47%(8.42%) (mean (SD)) in accuracy over state-of-the-art methods for one public dataset, and 5.75%(4.52%) for another public dataset with comparable performance to two existing methods. Further, it is computationally efficient with a reduction of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$18-97\%$$\end{document} in floating point operations as compared to existing methods.