Computational and Mathematical Methods in Medicine / 2017 / Article / Tab 20 / Review Article
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey Table 20 -means Cluster Algorithm and Self-Organizing Map for breast image classification.
Reference Descriptor Image Type No. of Images Key Findings Lashkari and Firouzmand [160 ] Thermogram 23 Both FCM method and Adaboost method utilized separately to classify images. For the classification purposes selected 23 features and also select the best features using feature selection algorithm. When they used the FCM method, the obtained Mean Accuracy was 75.00% whereas the Adaboost method Accuracy was 88.00%.Nattkemper et al. [161 ] MRI — -means algorithm as well as SM method utilized.Slazar-Licea et al. [162 ]. — Fuzzy -means algorithm used.Marcomini et al. [163 ] 24 morphological featuresUltrasound 144 Minimizing noise using Wiener filter, equalized and Median filter Obtained Sensitivity 100% and Specificity 78.00%.Chen et al. [164 ] 24 autocorrelation texture featuresUltrasound 243 Obtained ROC area . Accuracy 85.60%, Specificity 70.80%.Iscan et al. [165 ] Two-dimensional discrete cosine transform 2D continuous wavelet transformUltrasound — Automated threshold scheme introduce to increase the robustness of the SOM algorithm.