  Breast cancer is a common cancer for women. Early detection of breast cancer
can considerably increase the survival rate of women. This paper mainly focuses
on transfer learning process to detect breast cancer. Modified VGG (MVGG),
residual network, mobile network is proposed and implemented in this paper.
DDSM dataset is used in this paper. Experimental results show that our proposed
hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an
accuracy of 88.3% where the number of epoch is 15. On the other hand, only
modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet
provides an accuracy of 77.2%. So, it is clearly stated that the proposed
hybrid pre-trained network outperforms well compared to single architecture.
This architecture can be considered as an effective tool for the radiologists
in order to reduce the false negative and false positive rate. Therefore, the
efficiency of mammography analysis will be improved.
