Fusion of B-mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma
Article
Article Title | Fusion of B-mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma |
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ERA Journal ID | 17851 |
Article Category | Article |
Authors | Pham, The-Hanh, Faust, Oliver, Koh, Joel En Wei, Ciaccio, Edward J., Barua, Prabal D., Omar, Norlia, Ng, Wei Lin, Mumin, Nazimah Ab, Rahmat, Kartini and Acharya, U. Rajendra |
Journal Title | Expert Systems: the journal of knowledge engineering |
Journal Citation | 39 (5), pp. 1-19 |
Article Number | e12947 |
Number of Pages | 19 |
Year | 2022 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 0266-4720 |
1468-0394 | |
Digital Object Identifier (DOI) | https://doi.org/10.1111/exsy.12947 |
Web Address (URL) | https://onlinelibrary.wiley.com/doi/10.1111/exsy.12947 |
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 |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4003. Biomedical engineering |
Funder | Universiti Malaya |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Sheffield Hallam University, United Kingdom | |
Columbia University, United States | |
School of Business | |
University of Malaya, Malaysia | |
MARA University of Technology, Malaysia | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/yyw9v/fusion-of-b-mode-and-shear-wave-elastography-ultrasound-features-for-automated-detection-of-axillary-lymph-node-metastasis-in-breast-carcinoma
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