Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps
Abstract
:1. Introduction
2. Microwave Sensing
3. Methodology
3.1. Neural Network Design
3.2. Dataset Characteristics
3.3. Network Training
3.4. Performance Assessment
4. Results
4.1. Visual Analysis
4.2. Classification
4.3. Distance between Tumor Centers
4.4. Pixel-Wise Image Similarity
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
MWI | microwave imaging |
CNN(s) | convolutional neural network(s) |
2D | two-dimensional |
NN(s) | neural network(s) |
US | ultrasound |
MRI | magnetic resonance imaging |
ISP | inverse scattering problem |
EM | electromagnetic |
MoM | method of moments |
BCELoss | binary cross-entropy loss |
NCC | normalised cross-correlation |
NRMSE | normalised root mean square error |
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Predicted | |||
---|---|---|---|
Positive | Negative | ||
Actual | Positive | 7993 | 3 |
Negative | 5 | 7999 |
Metric | Formula | Value |
---|---|---|
Accuracy | 0.9995 | |
Sensitivity (Recall) | 0.9996 | |
Specificity | 0.9994 | |
Precision | 0.9994 | |
F1 Score | 0.9995 |
Metric | Mean | Standard Deviation |
---|---|---|
Soft-Dice | 0.144 | 0.078 |
NCC | 0.337 | 0.148 |
NRMSE | 0.809 | 0.057 |
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Borghouts, M.; Ambrosanio, M.; Franceschini, S.; Autorino, M.M.; Pascazio, V.; Baselice, F. Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps. Bioengineering 2023, 10, 1153. https://doi.org/10.3390/bioengineering10101153
Borghouts M, Ambrosanio M, Franceschini S, Autorino MM, Pascazio V, Baselice F. Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps. Bioengineering. 2023; 10(10):1153. https://doi.org/10.3390/bioengineering10101153
Chicago/Turabian StyleBorghouts, Marijn, Michele Ambrosanio, Stefano Franceschini, Maria Maddalena Autorino, Vito Pascazio, and Fabio Baselice. 2023. "Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps" Bioengineering 10, no. 10: 1153. https://doi.org/10.3390/bioengineering10101153