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

Table 19

-means Cluster Algorithm and Self-Organizing Map for breast image classification.

Reference	Descriptor	Image type	Number of images	Key findings
Moftah et al. [142]	Intensity distribution used as feature.	MRI	—	Three types of evaluation measures performed: (a) Accuracy, (b) feature based, (c) shape based measure.; This can classify the data as well as identify the target.; The obtained best Accuracy of the segmented ROI is 90.83%
Lee et al. [143]	1734 signal patterns.	MRI	322	Available signal patterns have been classified into 10 classes.
Dalmiya et al. [144]	Discrete Wavelet Transform.	Mammogram	—	Cancer tumor masses have been segmented.
Elmoufidi et al. [145]	Local Binary Pattern.	Mammogram	322	Image enhancing.; Generation of number of clusters; Detection of regions of interest.; Mean detection of regions of interest is 85.00%.
Samundeeswari et al. [146]		Ultrasound	—	Utilizing ant colony and regularization parameters.; This method obtained 96.00% similarity between segmented and reference tumors.
Rezaee [147]	Discrete Wavelet Transform.	Mammogram	120	Early detection of tumors from the breast image.; Tumor detection Accuracy 92.32%, Sensitivity 90.24%.
Chandra et al. [148]	Gray intensity values.	Mammogram	—	Mammogram image has been clustered using SOM along with the Quadratic Neural Network.