Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning
Abstract
1. Introduction
2. Methods
2.1. Study Subjects
2.2. Data Preprocessing
2.3. Dataset Construction
2.4. Training Convolutional Neural Networks (CNNs)
2.5. Gradient-Weighted Class Activation Mapping (Grad-CAM)
2.6. Meta-Analysis
2.7. Statistical Analysis
3. Results
3.1. Clinical Demographics of Subjects
3.2. Performance of CNN Models for Breast Cancer Detection
3.3. Sub-Group Analyses
3.4. Grad-CAM
3.5. Meta-Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Whole Dataset | Training Set | Test Set | |||||
---|---|---|---|---|---|---|---|
Breast n | Patient n | Breast n | Patient n | Breast n | Patient n | ||
Overall | 3002 | 1501 | 2701 | 1484 | 301 | 284 | |
Non-malignant | 2465 | 1496 | 2218 | 1427 | 247 | 235 | |
Malignant | 537 | 532 | 483 | 478 | 54 | 54 | |
Breast density | A | 152 | 76 | 132 | 74 | 20 | 18 |
B | 594 | 297 | 532 | 292 | 62 | 57 | |
C | 1560 | 780 | 1405 | 774 | 155 | 149 | |
D | 696 | 348 | 632 | 344 | 64 | 60 |
Breast Density/Model | Accuracy (%) | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | AUC |
---|---|---|---|---|---|---|
Overall | ||||||
DenseNet-169 | 88.1 ± 0.2 | 87.0 ± 0.0 | 88.4 ± 0.2 | 62.1 ± 0.5 | 96.9 ± 0.0 | 0.952 ± 0.005 |
EfficientNet-B5 | 87.9 ± 4.7 | 88.3 ± 4.7 | 87.9 ± 4.7 | 62.1 ± 9.9 | 97.2 ± 1.3 | 0.954 ± 0.020 |
Density A | ||||||
DenseNet-169 | 95.0 ± 0.0 | 100 ± 0.0 | 92.9 ± 0.0 | 85.7 ± 0.0 | 100.0 ± 0.0 | 0.984 ± 0.007 |
EfficientNet-B5 | 96.7 ± 2.9 | 100.0 ± 0.0 | 95.3 ± 4.1 | 90.5 ± 8.3 | 100.0 ± 0.0 | 0.988 ± 0.012 |
Density B | ||||||
DenseNet-169 | 96.2 ± 4.1 | 97.0 ± 5.3 | 96.1 ± 3.9 | 85.3 ± 14.3 | 99.3 ± 1.2 | 0.962 ± 0.041 |
EfficientNet-B5 | 95.2 ± 4.3 | 97.0 ± 5.3 | 94.8 ± 4.1 | 81.0 ± 12.9 | 99.3 ± 1.2 | 0.990 ± 0.009 |
Density C | ||||||
DenseNet-169 | 86.4 ± 6.2 | 87.7 ± 4.3 | 86.2 ± 6.7 | 58.8 ± 13.5 | 97.0 ± 1.1 | 0.950 ± 0.014 |
EfficientNet-B5 | 81.9 ± 5.1 | 84.0 ± 5.7 | 81.5 ± 5.2 | 49.6 ± 9.1 | 96.0 ± 1.6 | 0.940 ± 0.016 |
Density D | ||||||
DenseNet-169 | 84.3 ± 5.4 | 83.3 ± 5.8 | 84.6 ± 5.3 | 51.0 ± 11.5 | 96.5 ± 1.3 | 0.902 ± 0.033 |
EfficientNet-B5 | 85.9 ± 10.9 | 86.7 ± 11.5 | 85.8 ± 10.8 | 58.4 ± 28.2 | 97.1 ± 2.5 | 0.925 ± 0.055 |
(a) Sensitivity | |||
---|---|---|---|
Sensitivity (95% CI) | |||
Regab (2019) | 0.86 | (0.79–0.91) | |
Rodriguez–Ruiz (2019) | 0.86 | (0.78–0.92) | |
Gastounioti (2018) | 0.81 | (0.72–0.88) | |
Kim (2018) | 0.76 | (0.72–0.79) | |
Becker (2017) | 0.71 | (0.63–0.79) | |
Teare (2017) | 0.91 | (0.86–0.95) | |
Akselrob-Ballin (2019) | 0.80 | (0.79–0.81) | |
Cai (2019) | 0.89 | (0.86–0.92) | |
Al-Masni (2018) | 0.99 | (0.96–1.00) | |
Casti (2017) | 0.84 | (0.64–0.95) | |
Sun (2017) | 0.81 | (0.79–0.83) | |
Wang (2016) | 0.89 | (0.81–0.94) | |
Pooled sensitivity = 0.81 (0.80–0.82) | |||
I2 = 0.927 | |||
(b) Specificity | |||
Specificity (95% CI) | |||
Regab (2019) | 0.88 | (0.82–0.92) | |
Rodriguez–Ruiz (2019) | 0.79 | (0.72–0.86) | |
Gastounioti (2018) | 0.98 | (0.96–0.99) | |
Kim (2018) | 0.90 | (0.88–0.92) | |
Becker (2017) | 0.70 | (0.62–0.77) | |
Teare (2017) | 0.80 | (0.76–0.84) | |
Akselrob-Ballin (2019) | 0.82 | (0.80–0.83) | |
Cai (2019) | 0.87 | (0.83–0.90) | |
Al-Masni (2018) | 1.00 | (0.98–1.00) | |
Casti (2017) | 0.77 | (0.55–0.92) | |
Sun (2017) | 0.72 | (0.70–0.74) | |
Wang (2016) | 0.90 | (0.82–0.95) | |
Pooled specificity = 0.82 (0.81–0.82) | |||
I2 = 0.967 |
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Suh, Y.J.; Jung, J.; Cho, B.-J. Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning. J. Pers. Med. 2020, 10, 211. https://doi.org/10.3390/jpm10040211
Suh YJ, Jung J, Cho B-J. Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning. Journal of Personalized Medicine. 2020; 10(4):211. https://doi.org/10.3390/jpm10040211
Chicago/Turabian StyleSuh, Yong Joon, Jaewon Jung, and Bum-Joo Cho. 2020. "Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning" Journal of Personalized Medicine 10, no. 4: 211. https://doi.org/10.3390/jpm10040211
APA StyleSuh, Y. J., Jung, J., & Cho, B.-J. (2020). Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning. Journal of Personalized Medicine, 10(4), 211. https://doi.org/10.3390/jpm10040211