Review Article
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey
Table 7
Neural Network for breast image classification.
| Reference | Descriptor | Image type | Number of images | Key findings |
| Chen et al. [61] | Variance Contrast of Wavelet Coefficient | Ultrasound | 242 | The achieved ROC curve 0.9396 0.0183 | Autocorrelation of Wavelet Coefficient |
| Silva et al. [62]
| 22 different morphological features such as convexity and lobulation have been utilized | Ultrasound | — | The best obtained Accuracy and ROC curve are 96.98% and 0.98, respectively |
| Saritas [63] | Age of patient, mass shape, mass border, Mass density, BIRADS | Mammogram | — | Disease prediction rate is 90.5% | Neural Network utilized 5 neurons in input layers and one hidden layer. |
| López-Meléndez et al. [64] | Area, perimeter, etc. have been utilized | Mammogram | 322 | The achieved Sensitivity and Specificity are 96.29% and 99.00%, respectively. |
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