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A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
CNN Models | Layer Name1 | Number of features |
---|---|---|
ResNet50 | avg_pool | 2048 |
AlexNet | fc8_prefletten | 4096 |
MobileNetSmall | Logits | 1000 |
ConvNeXtSmall | head_layer | 768 |
EfficientNet | avg_pool | 1280 |
CNN Models | Layer Name1 | Number of features |
---|---|---|
ResNet50 | avg_pool | 2048 |
AlexNet | fc8_prefletten | 4096 |
MobileNetSmall | Logits | 1000 |
ConvNeXtSmall | head_layer | 768 |
EfficientNet | avg_pool | 1280 |
Dataset | Before feature selection | After feature selection |
---|---|---|
RSNA | 183851 | 452 |
MIAS | 9192 | 212 |
DDSM | 9192 | 206 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 81% | 84% | 87% | 0.82 | 0.86 |
Resnet50 | 84% | 90% | 86% | 0.89 | 0.88 |
MobileNetSmall | 77% | 85% | 81% | 0.81 | 0.83 |
ConvNexSmall | 79% | 87% | 83% | 0.83 | 0.85 |
EfficientNet | 86% | 92% | 88% | 0.92 | 0.90 |
Concat. Model | 92% | 96% | 92% | 0.96 | 0.94 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 73% | 70% | 72% | 0.70 | 0.71 |
Resnet50 | 72% | 75% | 71% | 0.73 | 0.73 |
MobileNetSmall | 64% | 71% | 67% | 0.68 | 0.69 |
ConvNexSmall | 66% | 74% | 70% | 0.71 | 0.72 |
EfficientNet | 71% | 78% | 74% | 0.76 | 0.76 |
Concat. Model | 78% | 81% | 79% | 0.82 | 0.80 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 71% | 67% | 69% | 0.68 | 0.68 |
Resnet50 | 69% | 70% | 67% | 0.71 | 0.68 |
MobileNetSmall | 60% | 67% | 63% | 0.64 | 0.65 |
ConvNexSmall | 62% | 69% | 65% | 0.67 | 0.67 |
EfficientNet | 73% | 74% | 70% | 0.75 | 0.72 |
Concat. Model | 78% | 79% | 77% | 0.80 | 0.78 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 62% | 61% | 63% | 0.62 | 0.62 |
Resnet50 | 64% | 66% | 63% | 0.65 | 0.64 |
MobileNetSmall | 60% | 63% | 59% | 0.60 | 0.61 |
ConvNexSmall | 62% | 65% | 61% | 0.63 | 0.63 |
EfficientNet | 68% | 70% | 66% | 0.68 | 0.68 |
Concat. Model | 73% | 75% | 72% | 0.74 | 0.73 |
Method | Dataset | Number Of Images | ACC | Sn | Pr |
---|---|---|---|---|---|
SVM & Hough[29] | MIAS & InBreast | 322&206 | 86.13% | 80.67% | 92.81% |
LQP & SVM [30] | MIAS | 95 | 94% | NA | NA |
GMM & SVM [31] | Mini-MIAS dataset | 90 | 92.5% | NA | NA |
KNN [32] | Mini-MIAS | 120 | 92% | NA | NA |
Voting Classifier[33] | MIAS | 322 | 85% | NA | NA |
CNN-4d [34] | Mini -MIAS | 547 | 89.05% | 90.63% | 83.67% |
CNN [35] | DDSM | 10,480 | 93.5% | NA | NA |
CNNs [36] | DDSM | 11,218 | 85.82% | 82.28% | 86.59% |
Our Method+NN | MIAS | 322 | 94.5% | 96.32% | 91.80% |
Our Method+NN | DDSM | 55,890 | 96% | 94.70% | 97% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Submitted:
29 May 2023
Posted:
31 May 2023
You are already at the latest version
A peer-reviewed article of this preprint also exists.
This version is not peer-reviewed
Submitted:
29 May 2023
Posted:
31 May 2023
You are already at the latest version
CNN Models | Layer Name1 | Number of features |
---|---|---|
ResNet50 | avg_pool | 2048 |
AlexNet | fc8_prefletten | 4096 |
MobileNetSmall | Logits | 1000 |
ConvNeXtSmall | head_layer | 768 |
EfficientNet | avg_pool | 1280 |
CNN Models | Layer Name1 | Number of features |
---|---|---|
ResNet50 | avg_pool | 2048 |
AlexNet | fc8_prefletten | 4096 |
MobileNetSmall | Logits | 1000 |
ConvNeXtSmall | head_layer | 768 |
EfficientNet | avg_pool | 1280 |
Dataset | Before feature selection | After feature selection |
---|---|---|
RSNA | 183851 | 452 |
MIAS | 9192 | 212 |
DDSM | 9192 | 206 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 81% | 84% | 87% | 0.82 | 0.86 |
Resnet50 | 84% | 90% | 86% | 0.89 | 0.88 |
MobileNetSmall | 77% | 85% | 81% | 0.81 | 0.83 |
ConvNexSmall | 79% | 87% | 83% | 0.83 | 0.85 |
EfficientNet | 86% | 92% | 88% | 0.92 | 0.90 |
Concat. Model | 92% | 96% | 92% | 0.96 | 0.94 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 73% | 70% | 72% | 0.70 | 0.71 |
Resnet50 | 72% | 75% | 71% | 0.73 | 0.73 |
MobileNetSmall | 64% | 71% | 67% | 0.68 | 0.69 |
ConvNexSmall | 66% | 74% | 70% | 0.71 | 0.72 |
EfficientNet | 71% | 78% | 74% | 0.76 | 0.76 |
Concat. Model | 78% | 81% | 79% | 0.82 | 0.80 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 71% | 67% | 69% | 0.68 | 0.68 |
Resnet50 | 69% | 70% | 67% | 0.71 | 0.68 |
MobileNetSmall | 60% | 67% | 63% | 0.64 | 0.65 |
ConvNexSmall | 62% | 69% | 65% | 0.67 | 0.67 |
EfficientNet | 73% | 74% | 70% | 0.75 | 0.72 |
Concat. Model | 78% | 79% | 77% | 0.80 | 0.78 |
CNN Models | Acc | Sn | Pr | AUC | F-Score |
---|---|---|---|---|---|
AlexNet | 62% | 61% | 63% | 0.62 | 0.62 |
Resnet50 | 64% | 66% | 63% | 0.65 | 0.64 |
MobileNetSmall | 60% | 63% | 59% | 0.60 | 0.61 |
ConvNexSmall | 62% | 65% | 61% | 0.63 | 0.63 |
EfficientNet | 68% | 70% | 66% | 0.68 | 0.68 |
Concat. Model | 73% | 75% | 72% | 0.74 | 0.73 |
Method | Dataset | Number Of Images | ACC | Sn | Pr |
---|---|---|---|---|---|
SVM & Hough[29] | MIAS & InBreast | 322&206 | 86.13% | 80.67% | 92.81% |
LQP & SVM [30] | MIAS | 95 | 94% | NA | NA |
GMM & SVM [31] | Mini-MIAS dataset | 90 | 92.5% | NA | NA |
KNN [32] | Mini-MIAS | 120 | 92% | NA | NA |
Voting Classifier[33] | MIAS | 322 | 85% | NA | NA |
CNN-4d [34] | Mini -MIAS | 547 | 89.05% | 90.63% | 83.67% |
CNN [35] | DDSM | 10,480 | 93.5% | NA | NA |
CNNs [36] | DDSM | 11,218 | 85.82% | 82.28% | 86.59% |
Our Method+NN | MIAS | 322 | 94.5% | 96.32% | 91.80% |
Our Method+NN | DDSM | 55,890 | 96% | 94.70% | 97% |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Zahra Jafari
et al.
,
2023
Luana Conte
et al.
,
2020
Tengku Muhammad Hanis
et al.
,
2023
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