Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment
Abstract
:Simple Summary
Abstract
1. Introduction
2. Material and Methods
2.1. Electromagnetic Properties of Tissue
2.2. The Microwave Imaging Problem
2.3. Deep Learning Microwave Imaging Framework for HT Temperature Monitoring
2.3.1. Microwave Imaging Processing
2.3.2. Deep Learning Architecture for Classification
2.4. Assessment of the DL-MWI Framework for Temperature Monitoring in Neck Tumor Hyperthermia
2.4.1. Anatomical Model
2.4.2. MWI Simulations
2.4.3. MWI Imaging Results
2.4.4. CNN Implementation: Categorical Labels
2.4.5. CNN Implementation: Training
2.4.6. Performance Assessment Metrics
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Class | DSC | MCC |
---|---|---|
Unheated | 0.953 | 0.928 |
Therapeutic | 0.932 | 0.887 |
Hot | 0.912 | 0.878 |
Class | DSC | MCC |
---|---|---|
Unheated | 0.920 | 0.855 |
Hot | 0.907 | 0.855 |
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Yago Ruiz, Á.; Cavagnaro, M.; Crocco, L. Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment. Cancers 2023, 15, 1717. https://doi.org/10.3390/cancers15061717
Yago Ruiz Á, Cavagnaro M, Crocco L. Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment. Cancers. 2023; 15(6):1717. https://doi.org/10.3390/cancers15061717
Chicago/Turabian StyleYago Ruiz, Álvaro, Marta Cavagnaro, and Lorenzo Crocco. 2023. "Hyperthermia Treatment Monitoring via Deep Learning Enhanced Microwave Imaging: A Numerical Assessment" Cancers 15, no. 6: 1717. https://doi.org/10.3390/cancers15061717