Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Dataset for Internal Validation
2.1.2. Dataset for External Validation
2.2. Toolbox for Radiomics Analysis: matRadiomics
2.3. Preprocessing
- CLAHE increases contrast in low-contrast mammographic images while minimizing noise amplification.
- The median filter reduces noise while preserving critical image structures, outperforming traditional average filters [52].
- Improve the visibility of fine details and textures, making key diagnostic patterns more evident, which helps both traditional radiomics and DL models extract more informative features.
- Reduce the impact of lighting or acquisition variations across images, leading to more consistent input data for the model.
- Improve the discrimination performance because better feature representation often translates into better performance, especially in distinguishing small lesions or subtle abnormalities.
2.4. Radiomics Features
- First-order features describe the intensity distribution within the region of interest (ROI), including metrics such as mean, standard deviation, skewness, and kurtosis.
- Second-order features analyze texture by assessing the spatial relationships between voxel intensities, capturing patterns that reflect tissue heterogeneity.
- Shape features define the geometry and morphology of the ROI, including volume, surface area, and sphericity. These are particularly useful for distinguishing lesions, as benign ones tend to be more regular, while malignant ones often exhibit irregular shapes.
2.4.1. Feature Extraction
2.4.2. Feature Selection
2.5. Machine Learning Predictive Models
2.5.1. Linear Discriminant Analysis
- Within-class scatter matrix (): measures the variance within each class and should be minimized for optimal classification.
- Between-class scatter matrix (): measures the variance between class means and should be maximized.
2.5.2. Support Vector Machine
2.6. Performance Metrics
- True positives (TP): positive examples correctly classified as positive.
- True negatives (TN): negative examples correctly classified as negative.
- False positives (FP): negative examples incorrectly classified as positive.
- False negatives (FN): positive examples incorrectly classified as negative.
- An AUC of 1 indicates perfect classification, where the model distinguishes all positive from negative instances.
- An AUC of 0 suggests a completely inverted classifier.
- An AUC of 0.5 indicates random guessing, with no predictive power.
2.7. Deep Radiomics
2.7.1. Implementation of EfficientnetB6
2.7.2. Dataset Preparation for the Neural Network
- Horizontal and vertical flips.
- Random translations and rotations (±20°).
- Brightness and contrast adjustments.
- Gaussian filtering and elastic deformations.
2.7.3. Model Training Configuration
3. Results
3.1. CBIS-DDSM Database
3.2. Preprocessing Results
3.3. Machine Learning-Based Radiomics
3.4. Deep Learning
4. Discussion
- The integration of a fully automated radiomics workflow based on matRadiomics, which enables end-to-end radiomics processing from image visualization to classification within a unified platform. This integrated solution distinguishes our approach from others that require separate tools for different steps.
- A direct comparison between a radiomics workflow and a DL architecture, focusing on the classification of both masses and microcalcifications—a combination that is not consistently addressed together in the previous literature.
- An external validation to add an important dimension of generalizability, which is often lacking in similar studies.
- Preprocessing techniques (CLAHE and median filtering) were applied to improve image quality [72] by reducing noise and artifacts and enhancing radiomics feature extraction, a step that is often underrepresented in similar comparative studies.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ROC AUC | Test ROC AUC | Accuracy | Test Accuracy |
---|---|---|---|
97.42% | 97.08% | 95% | 94% |
Classification Type | AUC | Accuracy | Recall | Specificity | Precision | F1-Score |
---|---|---|---|---|---|---|
Masses | 61.48% | 63.01% | 58.33% | 67.57% | 63.64% | 60.87% |
Calcifications | 66.73% | 64.86% | 60% | 73.17% | 79.25% | 68.29% |
Classification Type | AUC | Accuracy | Recall | Specificity | Precision | F1-Score |
---|---|---|---|---|---|---|
Masses | 81.52% | 78% | 66.70% | 85.28%% | 75.39% | 70.25% |
Calcifications | 76.24% | 71.1% | 85.78% | 48.84% | 81.96% | 78.24% |
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Stefano, A.; Bini, F.; Giovagnoli, E.; Dimarco, M.; Lauciello, N.; Narbonese, D.; Pasini, G.; Marinozzi, F.; Russo, G.; D’Angelo, I. Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography. Diagnostics 2025, 15, 953. https://doi.org/10.3390/diagnostics15080953
Stefano A, Bini F, Giovagnoli E, Dimarco M, Lauciello N, Narbonese D, Pasini G, Marinozzi F, Russo G, D’Angelo I. Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography. Diagnostics. 2025; 15(8):953. https://doi.org/10.3390/diagnostics15080953
Chicago/Turabian StyleStefano, Alessandro, Fabiano Bini, Eleonora Giovagnoli, Mariangela Dimarco, Nicolò Lauciello, Daniela Narbonese, Giovanni Pasini, Franco Marinozzi, Giorgio Russo, and Ildebrando D’Angelo. 2025. "Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography" Diagnostics 15, no. 8: 953. https://doi.org/10.3390/diagnostics15080953
APA StyleStefano, A., Bini, F., Giovagnoli, E., Dimarco, M., Lauciello, N., Narbonese, D., Pasini, G., Marinozzi, F., Russo, G., & D’Angelo, I. (2025). Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography. Diagnostics, 15(8), 953. https://doi.org/10.3390/diagnostics15080953