Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Table 1

List of studies used different deep segmentation methods of breast cancer mammograms images.

Ref#	Year	Segmentation method	Segmentation accuracy (dice coefficient index)	Classifier	Dataset	Classification accuracy
[37]	2020	Vanilla U-net	95.1%	VGG-16	CBIS-DDSM, INbreast, UCHCDM, BCDR-01	92.6%
[32]	2019	RU-Net	98.3%	ResNet	INbreast	98.7%
[34]	2019	U-Net integrated AGs	82.24%	—	DDSM	78.38%
[29]	2018	FrCN	92.69%	CNN	INbreast	95.64%
[35]	2015	CRF	90%	—	DDSM-BCRP and INbreast	—
[33]	2020	cGAN	98%	CNN based on BI-RADS	Abreast	97.85%
[39]	2020	DSPAE	—	Linear classifier	MIAS; DDSM	97.54%; 98.13%