Computational and Mathematical Methods in Medicine / 2017 / Article / Tab 12 / Review Article
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey Reference Descriptor Image type Number of images Key findings Beura et al. [95 ] Two-dimensional discrete orthonormal -transform has been used for the feature extractionMammogram — Achieved Accuracy and AUC values on MIAS database are 98.3%, 0.9985. Achieved Accuracy and AUC values on DDSM database are 98.8%, 0.9992. Diz et al. [96 ] GLCMMammogram 410 Their achieved Accuracy value is 76.60% GLRLM Mean false positive value is 81.00%.Zhang et al. [97 ] 133 features (mass based and content based)Mammogram 400 Computer model has been created which is able to find a location that was not detected by trainee.Ahmad and Yusoff [98 ] Nine features selectedBiopsy 700 Achieved Sensitivity, Specificity, and Accuracy are 75.00%, 70.00%, and 72.00%, respectively.Paul et al. [99 ] Harlick texture featureHistopathological 50 Their achieved Recall and Precision are 81.13% and 83.50%.Chen et al. [100 ] Dual-tree complex wavelet transform (DT-CWT) has been used for the feature extraction.Mammogram — Achieved Received Operating Curve (ROC) 0.764.Zhang et al. [101 ] Curvelet Transform GLCM CLBPHistopathological 50 Random Subspace Ensemble (RSE) utilized. Their achieved classification Accuracy is 95.22% where the previous Accuracy on this same database was 93.40%.