Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Simulated Images Generated by Custom Algorithm
2.3. Image Classification
2.4. Statistical Significance
3. Results
3.1. Model Accuracy and Sample Feature Maps
3.2. Generated Simulated Cervix Tumor Images
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
RT | Radiation Therapy |
ResNet | Residual Neural Network |
ML | Machine Learning |
AI | Artificial Intelligence |
XAI | Explainable AI |
MRI | Magnetic Resonance Imaging |
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Provenzano, D.; Wang, J.; Goyal, S.; Rao, Y.J. Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer. Tomography 2025, 11, 38. https://doi.org/10.3390/tomography11030038
Provenzano D, Wang J, Goyal S, Rao YJ. Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer. Tomography. 2025; 11(3):38. https://doi.org/10.3390/tomography11030038
Chicago/Turabian StyleProvenzano, Destie, Jeffrey Wang, Sharad Goyal, and Yuan James Rao. 2025. "Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer" Tomography 11, no. 3: 38. https://doi.org/10.3390/tomography11030038
APA StyleProvenzano, D., Wang, J., Goyal, S., & Rao, Y. J. (2025). Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer. Tomography, 11(3), 38. https://doi.org/10.3390/tomography11030038