Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions
Abstract
:1. Introduction
2. Datasets and Methods
2.1. Datasets
2.2. Theoretical Scheme of Fermi–Dirac Correction Function
2.3. Experimental Framework
3. Results
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter/Function | Value/Method |
---|---|
Number of epochs | 30 |
Batch size | 32 |
Learning rate (Initial value) | 0.00015 |
Loss function | 3D soft dice loss function |
Optimizer | Adam |
Preprocessing Method | Dice Score (WT only) | Computational Time (min) |
---|---|---|
Null | 0.7183 | 91 |
z-score Normalization | 0.9296 | 142 |
3D Global Histogram Equalization | 0.9148 | 84 |
Gamma Correction 1 | 0.9319 | 93 |
correction | 0.9431 | 88 |
correction | 0.9347 | 90 |
Preprocessing Method | Validation Stage (WT Only) | ||||||
---|---|---|---|---|---|---|---|
TP | TN | FP | FN | Accuracy | Recall | Precision | |
Null | 15,306 | 789,342 | 2003 | 5886 | 0.9901 | 0.71 | 0.85 |
z-score Normalization | 19,512 | 789,570 | 1776 | 1680 | 0.9957 | 0.90 | 0.90 |
3D Global Histogram Equalization | 19,293 | 789,400 | 1947 | 1899 | 0.9952 | 0.89 | 0.89 |
Gamma Correction 1 | 18,603 | 790,100 | 1248 | 2562 | 0.9953 | 0.86 | 0.92 |
correction | 19,180 | 790,010 | 1339 | 2012 | 0.9959 | 0.89 | 0.91 |
correction | 19,471 | 789,500 | 1841 | 1721 | 0.9956 | 0.90 | 0.89 |
Preprocessing Method | Dice Score | Computational Time (min) | ||
---|---|---|---|---|
Training Stage | Validation Stage | |||
3D Global Histogram Equalization | WT: | 0.9220 | 0.8002 | 138 |
TC: | 0.9419 | 0.7688 | ||
ET: | 0.9142 | 0.6365 | ||
correction | WT: | 0.9491 | 0.8337 | 140 |
TC: | 0.9757 | 0.7976 | ||
ET: | 0.9559 | 0.6802 | ||
correction | WT: | 0.9336 | 0.8433 | 141 |
TC: | 0.9773 | 0.8041 | ||
ET: | 0.9606 | 0.6848 |
Preprocessing Method | Validation Stage | |||||||
---|---|---|---|---|---|---|---|---|
TP | TN | FP | FN | Accuracy | Recall | Precision | ||
3D Global Histogram Equalization | WT: | 18,910 | 789,880 | 1465 | 2281 | 0.9953 | 0.89 | 0.89 |
TC: | 7818 | 801,309 | 1490 | 1918 | 0.9958 | 0.80 | 0.84 | |
ET: | 3141 | 807,914 | 700 | 780 | 0.9981 | 0.80 | 0.82 | |
correction | WT: | 18,949 | 789,874 | 1470 | 2242 | 0.9954 | 0.89 | 0.93 |
TC: | 7627 | 801,691 | 1107 | 2110 | 0.9960 | 0.78 | 0.87 | |
ET: | 3007 | 808,156 | 459 | 914 | 0.9983 | 0.77 | 0.87 | |
correction | WT: | 19,240 | 789,955 | 1340 | 1952 | 0.9959 | 0.91 | 0.93 |
TC: | 7966 | 801,841 | 958 | 1772 | 0.9966 | 0.82 | 0.89 | |
ET: | 3153 | 808,200 | 415 | 768 | 0.9998 | 0.80 | 0.88 |
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Tai, Y.-L.; Huang, S.-J.; Chen, C.-C.; Lu, H.H.-S. Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions. Entropy 2021, 23, 223. https://doi.org/10.3390/e23020223
Tai Y-L, Huang S-J, Chen C-C, Lu HH-S. Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions. Entropy. 2021; 23(2):223. https://doi.org/10.3390/e23020223
Chicago/Turabian StyleTai, Yen-Ling, Shin-Jhe Huang, Chien-Chang Chen, and Henry Horng-Shing Lu. 2021. "Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions" Entropy 23, no. 2: 223. https://doi.org/10.3390/e23020223