Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network
Abstract
:1. Introduction
- We proposed a system for the classification of masses into benign and malignant using contextual information. It is based on the idea of an ensemble classifier and uses ResNet-50 as the backbone CNN model. It introduces diversity in decision-making using contextual information with multiple ROIs, not using multiple CNN models.
- For the system, we employed two schemes to extract multiple ROIs from a mass region for modeling diverse contextual information. For fusing the diverse contextual information from different ROIs, we used different fusing techniques to find the best one. Stacking gives the best results.
- We employed the ResNet-50 model as a backbone model. To examine the effect of breast density in the discrimination of benign and malignant masses, we introduced density-specific modifications of ResNet-50 based on the idea of fusing local and global features, knowing that when the density-specific model is used, the breast density must be known. The density-specific models result in better performance than ResNet-50. Finally, the impact of different BI-RADS types on the classification was evaluated.
- The mass regions of each type are not enough to fine-tune even a pre-trained model. We introduced a data augmentation approach for fine-tuning the pre-trained models.
Related Work
2. Proposed Method
2.1. Modeling the Context Information
2.1.1. Scale-Based Multi-Context Regions of Interest (ROIs) Extraction
2.1.2. Translation-Based Multi-Context ROIs Extraction
2.2. Preprocessing
2.3. Backbone Convolutional Neural Network (CNN) Model
2.4. Fine-Tuned ResNet-50
2.5. Density Specific Modification of ResNet-50–DResNet-50
2.5.1. DIResNet-50 for BI-RADS I
2.5.2. DIIResNet-50 for BI-RADS II
2.5.3. DIIIResNet-50 for BI-RADS III
2.5.4. DIVResNet-50 for BI-RADS IV
2.6. Fusion Techniques
2.6.1. Majority of the Decisions
2.6.2. Soft Voting
2.6.3. Max Voting
2.6.4. Stacking
2.7. Training of CNN Models
2.7.1. Datasets
- CBIS-DDSM [47]. The Curated Breast Imaging Subset of DDSM (CBIS-DDSM) is a challenging dataset that contains digitized film images of 753 calcifications and 891 masses converted to Digital Imaging and Communications in Medicine (DICOM) format. This dataset has the updated annotations of mass regions on mediolateral oblique (MLO) and bilateral craniocaudal (CC) views. The database size and ground truth verification make the DDSM a useful tool in developing and testing support systems without any bias. Using the annotations, we extracted benign and malignant mass regions. We only considered the mass abnormality with breast density. For evaluation, we used the protocol provided for this dataset; the dataset is separated into train and test datasets, as shown in Table 2. In the sequel, DT, D1T, D2T, D3T, D4T stand for the complete training data set (D1T∪D2T∪D3T∪D4T), and the training datasets for BI-RADS.I, BI-RADS.II, BI-RADS.III, BI-RADS.IV, respectively. Similarly, DTs, D1Ts, D2Ts, D3Ts, D4Ts represent the complete test data set (D1Ts∪D2Ts∪D3Ts∪D4Ts), and the test datasets for BI-RADS.I, BI-RADS.II, BI-RADS.III, BI-RADS.IV, respectively.
- INbreast [48]. It is the largest public dataset that contains 410 full-field digital mammographic (FFDM) images provided in Digital Imaging and Communications in Medicine (DICOM) format. Each case consists of mediolateral oblique (MLO) and bilateral craniocaudal (CC) views. According to the database size and ground truth verification, the INbreast provides useful data for building and testing support systems without bias. According to the annotation, the statistics of this dataset are given in Table 3.
2.7.2. Data Augmentation
2.7.3. Fine-Tuning the Backbone Models
3. Evaluation Protocol
- We used the evaluation protocol provided for CBIS-DDSM. The training set was used to fine-tune the backbone models; it was divided into training and validation sets with a ratio of 90:10. The new training set was utilized to fit the model independently, and the validation set was employed to control the training process. After completing a model’s training, its performance was evaluated on the test set of CBIS-DDSM without bias.
- For INbreast the cases are randomly divided into 80% for training,10% for validation, and 10% for testing, which allows us to run five-fold cross-validation. Cross-validation provides a less biased estimate of the model’s ability for unseen data.
4. Result
4.1. Why ResNet-50 as a Backbone Model?
4.2. The Effect of Multi-Scale and Multi-Context Schemes for a Test Region
4.3. The Effect of Different Fusion Techniques
4.4. The Effect of Density-Specific Models
4.5. Comparison with State-of-the-Art Methods
5. Discussion
- The system uses ResNet-50 and its modified versions as the backbone model. It would be better if a new data-dependent model is designed which is adaptive to mammogram images.
- The method fails when the mass appears in extremely dense breast tissue because the characteristic similarity between the dense tissue and masses makes breast mass classification difficult. Figure 16 shows mass regions of test images that are difficult to classify accurately.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Group | OUTPUT SIZE | 50-Layer |
---|---|---|
112 × 112 | 7 × 7, 64, stride 2 | |
56 × 56 | 3 × 3 max pool, stride 2 | |
28 × 28 | ||
14 × 14 | ||
7 × 7 | ||
1 × 1 | average pool, 1000-d fc, SoftMax |
Dataset | Dataset Set Pathology | Train | Test |
---|---|---|---|
D1 (BI-RADS.I) | Benign | 103 | 24 |
Malignant | 128 | 21 | |
D2 (BI-RADS.II) | Benign | 257 | 74 |
Malignant | 248 | 70 | |
D3 (BI-RADS.III) | Benign | 145 | 63 |
Malignant | 125 | 29 | |
D4 (BI-RADS.IV) | Benign | 53 | 21 |
Malignant | 38 | 12 | |
D (D1+D2+D3+D4) | Benign | 558 | 182 |
Malignant | 539 | 132 |
Dataset | Dataset Set Pathology | Number of Masses |
---|---|---|
D1 (BI-RADS.I) | Benign | 12 |
Malignant | 30 | |
D2 (BI-RADS.II) | Benign | 6 |
Malignant | 32 | |
D3 (BI-RADS.III) | Benign | 13 |
Malignant | 8 | |
D4 (BI-RADS.IV) | Benign | 6 |
Malignant | 1 | |
D (D1+D2+D3+D4) | Benign | 37 |
Malignant | 71 |
Model | Sen (%) | SP (%) | ACC (%) | Kappa (%) | F1-Score |
---|---|---|---|---|---|
ResNet-50 [32] | 99.24 | 87.36 | 92.36 | 84.70 | 91.61 |
DensNet-201 [52] | 81.01 | 97.44 | 89.17 | 78.40 | 88.28 |
InceptionResNetV2 [53] | 81.53 | 97.45 | 89.49 | 79 | 88.58 |
NasNetLarge [54] | 81.25 | 98.70 | 89.81 | 79.70 | 89.04 |
Scheme | Sen (%) | SP (%) | ACC (%) | Kappa (%) | F1-Score |
---|---|---|---|---|---|
single | 67.42 | 78.02 | 73.57 | 45.60 | 68.20 |
{5,10,15,20,25}-MS1 | 81.06 | 91.76 | 87.26 | 73.60 | 84.25 |
{10,20,30,40,50}-MS2 | 82.58 | 90.66 | 87.26 | 73.70 | 84.50 |
{50,60,70,80,100}-MS3 | 83.33 | 92.31 | 88.54 | 76.30 | 85.94 |
Scheme | Sen (%) | SP (%) | ACC (%) | Kappa (%) | F1-Score |
---|---|---|---|---|---|
single | 67.42 | 78.02 | 73.57 | 45.60 | 68.20 |
256 with 3 contexts-MC1 | 73.48 | 86.26 | 80.89 | 60.40 | 76.38 |
256 with 5 contexts-MC2 | 87.12 | 93.96 | 91.08 | 81.60 | 89.15 |
Fusion Technique | Sen (%) | SP (%) | Acc (%) | Kappa (%) | F1-Score | |
---|---|---|---|---|---|---|
Stacking | Random Forest | 87.88 | 93.96 | 91.40 | 82.30 | 89.59 |
SVM with RBF | 99.24 | 87.36 | 92.36 | 84.70 | 91.61 | |
SVM with Linear | 92.42 | 92.31 | 92.36 | 84.40 | 91.04 | |
SVM with Polynomial | 87.12 | 93.96 | 91.08 | 81.60 | 89.15 | |
Majority voting | 77.27 | 83.52 | 80.89 | 60.80 | 77.27 | |
Soft Voting | 78.79 | 84.62 | 82.17 | 63.10 | 78.79 | |
Max voting | 81.06 | 84.07 | 82.80 | 64.40 | 79.85 |
Models | Sen (%) | SP (%) | ACC (%) | Kappa (%) | F1-Score |
---|---|---|---|---|---|
DIRresNet-50_D1T_D1Ts | 100 | 91.67 | 95.56 | 91.12 | 95.45 |
DII RresNet-50_D2T_D2Ts | 92.86 | 87.84 | 90.28 | 80.57 | 90.28 |
DIII RresNet-50D3T_D3Ts | 96.55 | 96.83 | 96.74 | 92.50 | 94.92 |
DIVRresNet-50_D4T_D4Ts | 91.67 | 100 | 96.97 | 93.30 | 95.65 |
ResNet-50_D1T _D1Ts | 90.48 | 87.50 | 88.89 | 77.70 | 88.37 |
ResNet-50_D2T _D2Ts | 90 | 75.68 | 82.64 | 65.40 | 83.44 |
ResNet-50_D3T _D3Ts | 82.76 | 84.13 | 83.70 | 63.90 | 76.19 |
ResNet-50_D4T _ D4Ts | 66.67 | 100 | 87.88 | 71.80 | 80 |
Datasets | Models | Sen (%) | SP (%) | ACC (%) | Kappa (%) | F1-Score |
---|---|---|---|---|---|---|
CBIS-DDSM | DIRresNet-50_ DT_DTs | 98.48 | 92.31 | 94.90 | 89.65 | 94.20 |
DIIRresNet-50_DT_DTs | 96.97 | 92.31 | 94.27 | 88.36 | 93.43 | |
DIIIRresNet-50_DT_DTs | 97.73 | 91.21 | 93.95 | 87.75 | 93.14 | |
DIVRresNet-50_DT_DTs | 91.67 | 96.70 | 94.59 | 88.83 | 93.44 | |
RresNet-50_DT_ DTs | 99.24 | 87.36 | 92.36 | 84.67 | 91.61 | |
INbreast | DIRresNet-50_ DT_DTs | 100 | 97.5 | 99.09 | 98.07 | 99.31 |
DIIRresNet-50_DT_DTs | 100 | 100 | 100 | 100 | 100 | |
DIIIRresNet-50_DT_DTs | 97.14 | 97.5 | 97.19 | 94.31 | 97.77 | |
DIVRresNet-50_DT_DTs | 100 | 97.14 | 99 | 97.73 | 99.26 | |
RresNet-50_DT_ DTs | 98.57 | 97.5 | 98.18 | 96.14 | 98.61 |
References | Models\Descriptors | Sen (%) | SP (%) | AUC (%) | ACC (%) | |
---|---|---|---|---|---|---|
CBIS-DDSM | Khan et el. [8] | ResNet-50 | 75.46 | 62.75 | 69.10 | 69.98 |
MVFF | 81.82 | 72.02 | 76.90 | 77.66 | ||
Tsochatzidis et al. [10] | ResNet-50 from scratch | - | - | 80.40 | 74.90 | |
Fine-tuning ResNet-50 | - | - | 63.70 | 62.70 | ||
Duggento et al. [11] | AlexNet | 84.40 | 62.44 | - | 71.19 | |
AL Hakeem and Jang [13] | LBP-HOG | - | - | - | 64.35 | |
Li et al. [9] | Dual-core Net | - | - | 85 | - | |
Shu et al. [12] | Region-based Group-max Pooling | - | - | 83.3 | 76.2 | |
Global Group-max Pooling | - | - | 82.3 | 76.7 | ||
Proposed system | Multi-context ResNet-50 | 99.24 | 87.36 | 97.17 | 92.36 | |
Multi-context DIRresNet-50 | 98.48 | 92.31 | 94.38 | 94.90 | ||
Multi-context DIIRresNet-50 | 96.97 | 92.31 | 93.59 | 94.27 | ||
Multi-context DIIIRresNet-50 | 97.73 | 91.21 | 96.55 | 93.95 | ||
Multi-context DIVRresNet-50 | 91.67 | 96.70 | 94.83 | 94.59 | ||
INbreast | Chougrad et al. [14] | Resnet-50 | - | - | - | 92.50 |
Al-antari et al. [15] | Alex Net | 97.14 | 92.41 | 94.78 | 95.64 | |
Shen et al. [16] | ResCU-Net | - | - | 96.16 | 94.12 | |
Ghada et al. [17] | ResNet | - | - | - | 90 | |
Inception | - | - | - | 95 | ||
Lou et al. [18] | ResNet-50 | 69.23 | 74 | 84.96 | 72.37 | |
MGBN-50 | 77.16 | 88.24 | 93.11 | 84.50 | ||
Proposed system | Multi-context ResNet-50 | 98.57 | 97.500 | 99.02 | 98.18 | |
Multi-context DIRresNet-50 | 100 | 97.5 | 99.64 | 99.09 | ||
Multi-context DIIRresNet-50 | 100 | 100 | 100 | 100 | ||
Multi-context DIIIRresNet-50 | 97.14 | 97.50 | 97.35 | 97.19 | ||
Multi-context DIVRresNet-50 | 100 | 97.14 | 99.56 | 99 |
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Busaleh, M.; Hussain, M.; Aboalsamh, H.A.; Amin, F.-e.-. Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network. Biosensors 2021, 11, 419. https://doi.org/10.3390/bios11110419
Busaleh M, Hussain M, Aboalsamh HA, Amin F-e-. Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network. Biosensors. 2021; 11(11):419. https://doi.org/10.3390/bios11110419
Chicago/Turabian StyleBusaleh, Mariam, Muhammad Hussain, Hatim A. Aboalsamh, and Fazal-e- Amin. 2021. "Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network" Biosensors 11, no. 11: 419. https://doi.org/10.3390/bios11110419