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 features	Ultrasound	144	Minimizing noise using Wiener filter, equalized and Median filter; Obtained Sensitivity 100% and Specificity 78.00%.
Chen et al. [164]	24 autocorrelation texture features	Ultrasound	243	Obtained ROC area . Accuracy 85.60%, Specificity 70.80%.
Iscan et al. [165]	Two-dimensional discrete cosine transform; 2D continuous wavelet transform	Ultrasound	—	Automated threshold scheme introduce to increase the robustness of the SOM algorithm.