Review Article

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Table 9

Convolutional Neural Network.

ReferenceDescriptorImage typeNumber of imagesKey findings

Wu et al. [78]
Global FeaturesMammogram40 Achieved Sensitivity 75.00% and Specificity 75.00%.

Sahiner et al. [79] Global FeaturesMammogram168 The achieved ROC score is 0.87.

Lo et al. [80] Density, size, Shape, MarginMammogram144 The achieved ROC curve is 0.89.

Fonseca et al. [81] Global FeaturesMammogram— Breast density classification has been performed utilizing HT-L3 convolution.
Average achieved obtained Kappa value is 0.58.

Arevalo et al. [82] Global FeaturesMammogram736 The achieved ROC curve is 0.826.

Su et al. [83] Global FeaturesMammogram92 Fast Scanning CNN (fCNN) method has been utilized to reduce the information loss.
The average Precision, Recall, and 1 score are 91.00%, 82.00%, and 0.85, respectively.

Sharma and Preet [84] GLCM, GLDM GeometricalMammogram40 The best Accuracy achieved is 75.23% and 72.34%, respectively, for fatty and dense tissue classification.

Spanhol et al. [6] Global FeaturesHistopathology7909 The best Accuracy achieved 89 6.6%.

Rezaeilouyeh et al. [85] Local and Global Features Histopathology— Shearlet transform has been utilized for extracting local features.
When they utilize RGB image along with magnitude of Shearlet transform together, the Achieved Sensitivity, Specificity, and Accuracy were 84.00 1.00%, 91.00 2.00%, and 84.00 4.00%; when they utilize RGB image along with both the phase and magnitude of Shearlet transform together, the achieved Sensitivity, Specificity, and Accuracy were 89.00 1.00%, 94.00 1.00%, and 88.00 5.00%.