The breast cancer detection has received a great attention in the histopathology image classification. In this paper, a thresholded wavelet transformation with deep transfer learning has devised for breast cancer detection. The breast histopathology images are enhanced using thresholded wavelet transformation. Then, a fusion based deep transfer learning has employed to perform binary classification (benign/malignant) of breast histopathology images. The proposed fusion model has evaluated on Breast Cancer Histopathological (BreakHis) dataset and achieved 97.09% on 40X magnified images of the dataset. Further, the proposed model outperforms existing state-of-the-art models and pre-trained models in vital metrics.