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Reference | Descriptor | Image type | Number of images | Key findings |
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Rajakeerthana et al. [42] | GLCM, GLDM, SRDM, NGLCM, GLRM | Mammogram | 322 | The classifier achieved Accuracy. |
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Lessa and Marengoni [43]
| Mean, Median, Standard Deviation, Skewness, Kurtosis, Entropy, Range | Thermographic | 94 | Achieved Sensitivity, Specificity, and Accuracy are 87.00%, 83.00%, and 85.00%, respectively. |
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Wan et al. [44] | ALBP BBLBP | OCM | 46 | Achieved Sensitivity and Specificity are 100% and 85.20%. respectively. |
ROC value obtained 0.959. |
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Chen et al. [40] | 19 BI-RADS features have been used | Ultrasound | 238 | Chi squared method has been utilized for the feature selection. |
Achieved Accuracy, Sensitivity, and Specificity are 96.10%, 96.70%, and 95.70%, respectively. |
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de Lima et al. [45] | Total 416 features have been used | Mammogram | 355 | Multiresolution wavelet and Zernike moment have been utilized for the feature extraction. |
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Abirami et al. [46] | 12 statistical measures such as Mean, Median, and Max have been utilized as the features | Mammogram | 322 | Wavelet transform has been utilized for the feature extraction. |
The achieved Accuracy, Sensitivity, and Specificity are 95.50%, 95.00%, and 96.00%, respectively. |
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El Atlas et al. [47] | 13 morphological features have been utilized | Mammogram | 410 | Firstly the edge information has been utilized for the mass segmentation and then the morphological features were extracted. |
Achieved best Accuracy is 97.5%. |
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