Automated Breast Cancer Detection Models Based on Transfer Learning
Abstract
:1. Introduction
- Using various evaluation metrics such as accuracy, precision, recall (sensitivity), specificity, and F1-Score. Extensive comparative comparisons were performed to assess the effectiveness of the proposed systems;
- Mammograms show radiological indications that are readily detectable symptoms. As a result, deep learning-based methods can be used to automatically analyze mammograms, which significantly reduces analysis time;
- To fine-tune the weights of pre-trained networks on small datasets, as well as train the weights of networks on large datasets, a customized version of ResNet50 (MOD-RES) and a hybrid version of Nasnet and Mobile net were utilized;
- To improve the generalization effectiveness of the suggested method and prevent overfitting, a different training protocol assisted by different combinations of training policies (e.g., validation patience, and data augmentation) was used.
2. Related Works
3. Proposed System
3.1. MIAS Datasets
3.2. Image Pre-Processing
3.3. Proposed Learning Methods
3.3.1. Nasnet-Mobile
3.3.2. MOD-RES
4. Experimental Results
4.1. Assessment Metrices
4.2. Results of the Proposed Systems
4.3. Comparison to State-of-the-Art Methods
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.4 | 0.4 | 0.65 | 0.55 | 0.55 | 0.6 | 0.45 | 0.65 |
0.0004 | 0.55 | 0.5 | 0.6 | 0.6 | 0.55 | 0.6 | 0.55 | 0.5 |
0.0006 | 0.55 | 0.45 | 0.45 | 0.5 | 0.6 | 0.55 | 0.5 | 0.6 |
0.0008 | 0.55 | 0.65 | 0.55 | 0.6 | 0.65 | 0.6 | 0.65 | 0.7 |
Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.45 | 0.4 | 0.7 | 0.5 | 0.6 | 0.5 | 0.55 | 0.65 |
0.0004 | 0.5 | 0.5 | 0.55 | 0.45 | 0.55 | 0.55 | 0.55 | 0.55 |
0.0006 | 0.55 | 0.5 | 0.6 | 0.5 | 0.55 | 0.55 | 0.65 | 0.5 |
0.0008 | 0.45 | 0.55 | 0.55 | 0.6 | 0.65 | 0.55 | 0.5 | 0.45 |
Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.35 | 0.4 | 0.4 | 0.4 | 0.45 | 0.5 | 0.55 | 0.5 |
0.0004 | 0.4 | 0.45 | 0.55 | 0.4 | 0.6 | 0.55 | 0.45 | 0.45 |
0.0006 | 0.35 | 0.35 | 0.45 | 0.5 | 0.5 | 0.5 | 0.5 | 0.55 |
0.0008 | 0.4 | 0.4 | 0.55 | 0.6 | 0.7 | 0.6 | 0.55 | 0.5 |
Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.4 | 0.4 | 0.5 | 0.4 | 0.55 | 0.5 | 0.45 | 0.4 |
0.0004 | 0.35 | 0.5 | 0.35 | 0.35 | 0.45 | 0.5 | 0.4 | 0.4 |
0.0006 | 0.4 | 0.45 | 0.5 | 0.45 | 0.4 | 0.4 | 0.5 | 0.45 |
0.0008 | 0.35 | 0.35 | 0.45 | 0.4 | 0.5 | 0.45 | 0.4 | 0.5 |
Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.737 | 0.711 | 0.658 | 0.658 | 0.553 | 0.605 | 0.526 | 0.711 |
0.0004 | 0.5 | 0.789 | 0.658 | 0.711 | 0.763 | 0.737 | 0.632 | 0.763 |
0.0006 | 0.395 | 0.605 | 0.895 | 0.842 | 0.816 | 0.711 | 0.658 | 0.658 |
0.0008 | 0.553 | 0.526 | 0.711 | 0.684 | 0.526 | 0.789 | 0.579 | 0.763 |
Learning Rate | Ensemble Using Several Runs | |||||||
---|---|---|---|---|---|---|---|---|
Batch Size = 32 | Batch Size = 64 | Batch Size = 128 | Batch Size = 265 | |||||
1 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | |
0.0002 | 0.632 | 0.711 | 0.684 | 0.789 | 0.816 | 0.737 | 0.526 | 0.5 |
0.0004 | 0.5 | 0.526 | 0.632 | 0.789 | 0.868 | 0.737 | 0.711 | 0.5 |
0.0006 | 0.605 | 0.526 | 0.789 | 0.658 | 0.684 | 0.737 | 0.579 | 0.474 |
0.0008 | 0.658 | 0.763 | 0.605 | 0.737 | 0.658 | 0.632 | 0.579 | 0.526 |
Quantitative Measures | MOD-RES Model (Oversampling) | MOD-RES Model | Nasnet-Mobile Model |
---|---|---|---|
Overall Accuracy | 89.5 | 70 | 70 |
Precision | 89.5 | 64.3 | 83.3 |
Recall | 89.5 | 90 | 50 |
F1-score | 89.5 | 75 | 62.5 |
Recent Work | Technique | Dataset | Number of Images | Accuracy |
---|---|---|---|---|
Proposed Methodology | MOD-RES | MIAS |
| 89.5% |
Charan et al. [23] | CNN | MIAS |
| 65% |
Z. Hussain et al. [15] | VGG-16 | DDSM |
| 88% |
L. Falconi et al. [9] | VGG | CBIS-DDSM |
| 84.4% |
S. Eldin et al. [31] | DenseNet-169 | BACH |
| 82% |
S. Eldin et al. [31] | ResNet50 | BACH |
| 85% |
S. Eldin et al. [31] | ResNet101 | BACH |
| 88% |
S. Siddeeq et al. [32] | ResNet | INbreast |
| 85.9% |
K. Shaikh [19] | CNN | MIAS, DDSM, and BancoWeb LAPIMO |
| 87.5% |
S. Salvi and A. Kadam, [33] | CNN | Private Dataset |
| 87.84% |
W. Sun et al. [34] | CNN with Semi-Supervised Learning (SSL) algorithm | full-field digital mammography (FFDM) | 3158 region of interests (ROI) | 82.43% |
Roy et al. [35] | CNN | ICIAR 2018 |
| 87.4% |
S. Alanazi et al. [36] | CNN | Kaggle 162 H and E |
| 87% |
S. Singh et al. [37] | Histogram matching (HM) and DL fine-tuning | FFDM |
| 84.7% |
K. Mendel et al. [38] | CNN and SVM | FFDM |
| 89% |
A. Rodriguez-Ruiz et al. [39] | CNN | Private Dataset |
| 88% |
M. Yousefi et al. [40] | CCN | Research Laboratory at Massachusetts General Hospital (MGH) |
| 87% |
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Alruwaili, M.; Gouda, W. Automated Breast Cancer Detection Models Based on Transfer Learning. Sensors 2022, 22, 876. https://doi.org/10.3390/s22030876
Alruwaili M, Gouda W. Automated Breast Cancer Detection Models Based on Transfer Learning. Sensors. 2022; 22(3):876. https://doi.org/10.3390/s22030876
Chicago/Turabian StyleAlruwaili, Madallah, and Walaa Gouda. 2022. "Automated Breast Cancer Detection Models Based on Transfer Learning" Sensors 22, no. 3: 876. https://doi.org/10.3390/s22030876
APA StyleAlruwaili, M., & Gouda, W. (2022). Automated Breast Cancer Detection Models Based on Transfer Learning. Sensors, 22(3), 876. https://doi.org/10.3390/s22030876