Figure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure 6:
Performance analysis of various features in the proposed approach on DDSM dataset
| Feature selection | Classifier | Accuracy (%) | Sensitivity (%) | Specificity (%) | F1-score (%) |
|---|---|---|---|---|---|
| Raw images | RF | 92.20 | 91.80 | 92.50 | 92.00 |
| Raw images | DNN | 82.22 | 80.36 | 82.50 | 81.42 |
| Gabor | RF | 88.00 | 87.50 | 88.30 | 87.90 |
| GLCM | RF | 85.50 | 85.00 | 85.80 | 85.40 |
| Gabor + GLCM | SVM | 90.50 | 90.00 | 91.00 | 90.40 |
| Gabor + GLCM | DT | 92.98 | 94.66 | 90.16 | 92.60 |
| Gabor + GLCM | RF | 96.64 | 95.90 | 97.08 | 96.72 |
Summary of some related works on mammogram patch classification
| Ref. | Feature | Classifier | Feature dimension | Dataset | Accuracy (%) |
|---|---|---|---|---|---|
| [55] | GLCM | SVM | 200 × 1 | MIAS | 97.46 |
| [56] | Gabor + PCA | SVM | 86,400 × 1 | DDSM | 84 |
| [21] | Wavelet + GLCM | DNN | 120 × 1 | MIAS* (320) | 98.10 |
| DDSM* (550) | 99.40 | ||||
| [57] | RLTP | RF + SVM | 14 × 2 | MIAS* (376) | 90.00 |
| [58] | HOG, DSIFT, LCP | SVM | 24 × 1, 384 × 1, 80 × 1 | DDSM* (600) | 84 |
| [30] | FFST | SVM | 8776 × 1 | MIAS* (228) | 97 |
| 35857 × 1 | DDSM | 100 | |||
| [59] | Gabor filter | PSO + SVM | 1080 (9 OWs × 40 GFs × 3 SMs | DDSM* (1024) | 98.82 |
| Proposed | MSMS Gabor + GLCM | SVM | 128 × 1 | MIAS | 95.82 |
| DDSM | 93.32 | ||||
| MIAS | 95.91 | ||||
| Proposed | MSMS Gabor + GLCM | DT | 128 × 1 | DDSM | 94.87 |
| MIAS | 96.58 | ||||
| Proposed | MSMS Gabor + GLCM | RF | 128 × 1 | DDSM | 95.93 |
Literature survey on texture feature extraction techniques
| Technique | Description and findings | References |
|---|---|---|
| GLCM | It calculates the co-occurrence of pixel intensities to analyze texture. Effective for detecting breast cancer in mammography images | [9, 45] |
| GLRLM | Focuses on uniform run lengths of pixels. Demonstrates accuracy in predicting benign vs malignant breast masses. | [2] |
| Laws texture energy | Employs filters to extract texture information. Distinguishes between benign and malignant breast tumors. | [3] |
| Gabor filters | Uses bandpass filters for extracting texture features. Effective in differentiating benign and malignant tumors. | [4, 23] |
| Wavelet transform | Analyzes signals at multiple scales. Useful for detecting spiculated masses in mammograms. | [5, 21, 46] |
| Fractal dimensions | Helps analyze tumor shape for classification of benign vs malignant. Multi-fractal dimensions improve classification accuracy. | [14, 15] |
| Hybrid techniques | Combines LSRG with SWTs for early breast cancer diagnosis. | [18, 47] |
| Zernike moments | Captures image shape content and extracts features for mammogram classification. Achieves high sensitivity and specificity. | [34, 35] |
| EMD | Decomposes signals to intrinsic mode functions for automatic classification. | [37] |
| ELM | Combines shape, texture, and edge features for accurate classification of breast masses. | [42] |
| Hybrid features with CNN | Combines Gabor, Prewitt, and GLCM features with CNN for microcalcification detection, leveraging ReLU activation and data augmentation for improved accuracy and sensitivity. Achieved 89.56% accuracy and 82.14% sensitivity. | [43] |
| Lightweight CNN architectures | Employs lightweight CNNs with minimal layers for efficient mammogram analysis. Notable results include 99.30% accuracy and 95.00% sensitivity with INbreast dataset. | [44] |
| XAI | Integrates Gabor and GLCM features with XAI frameworks, aligning automated detection with interpretable outcomes for clinical acceptance. | [43] |
| Unified databases with augmentation | Tackles dataset imbalances by creating unified databases and employing rotation/scaling augmentations to improve model generalization. | [43, 44] |
Normal, benign, and malignant case distribution in the MIAS and DDSM datasets
| Dataset | Category | Total images | Description | ||
|---|---|---|---|---|---|
| Normal | Benign | Malignant | |||
| MIAS | 209 | 62 | 51 | 322 | Contains digitized mammograms from various sources, annotated with abnormalities by expert radiologists. |
| DDSM | 141 | 771 | 784 | 1,696 | Comprehensive database with mammograms, clinical information, and annotations, primarily used for breast cancer research and diagnosis. |