Fusion of B-mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma
Document Type
Article
Publication Date
6-1-2022
Abstract
In this study, we evaluate and compare the diagnostic performance of ultrasound for non-invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B-mode ultrasonography (USG) images. These images were subjected to pre-processing and feature extraction, based on bi-dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p-value, which was established with Student's t-test. The ranked features were used to train and test six classification algorithms with 10-fold cross-validation. Initially, we considered B-mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B-mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B-mode USG. Furthermore, there is scope in fusing SWE and B-mode USG to improve non-invasive ALN metastasis detection.
Keywords
Axillary lymph node, Cancer detection, Higher order spectra, Machine learning, Shear wave elastography, Ultrasound
Divisions
biomed
Funders
University of Malaya Faculty Research Grant [Grant No: FP017-2019],Fundamental Research Grant Scheme [Grant No: GPF06C-2018]
Publication Title
Expert Systems
Volume
39
Issue
5, SI
Publisher
Wiley
Publisher Location
111 RIVER ST, HOBOKEN 07030-5774, NJ USA