# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 1 Introduction
[9] investigated the effectiveness of transfer learning for breast histology images classification and evaluated the classification performance of the pre-trained networks of Inception-V3 and ResNet50. The experimental results showed that the Inception-V3 network outperformed the ResNet50 network achieved 97.08% and 96.66% respectively. Additionally, authors have applied some augmentation techniques, such as rotation and flipping to increase the number of training samples resulting in a total of 33,600 training and validation samples from the original 320 training samples. Nevertheless, authors should have assessed the effectiveness of the the augmentation techniques on more pre-trained models. Another interesting research [10] studied the transfer learning from a AlexNet to enhance the accuracy of lung nodule classification. Since AlexNet has been trained on ImageNet, there is no guarantee that deep features are suitable for the lung nodule classification. Hence, authors utilized the fine-tuning and feature selection techniques to enhance the transferability process. The results showed that the proposed technique can outperform the handcrafted texture descriptors. Nevertheless, this approach seems to be applicable only to AlexNet. Unlike the aforementioned works, we introduce the double-shot transfer learning (DSTL) technique by utilizing the most popular pre-trained networks in the literature (AlexNet [11], VGG-16 (visual geometry group) [12], VGG-19 [12], GoogLeNet [13], ResNet-50 [14], ResNet-101 [14], MobileNet-v2 [15], and ShuffleNet [16]). DSTL updates the learnable parameters of the pre-trained networks by fine-tuning them on 98,967 of the augmented X-ray images of benign and malignant breast cancers from CBIS-DDSM dataset. Then, the updated pre-trained networks are fine-tuned for the second time on the augmented images of the target mammographic datasets of mammographic image analysis society (MIAS) and breast cancer digital repository (BCDR) to differentiate benign from malignant breast cancer. The advantage of the DSTL over the single-shot transfer learning (SSTL) technique is that DSTL can improve the overall accuracy, sensitivity, specificity, area under the curve (AUC), training time, epoch number, and iteration number. The contribution of this paper can be summarized as follows:  
1. An effective technique based on the concept of transfer learning, called double-shot transfer learning (DSTL), is introduced to improve the overall accuracy and performance of the pre-trained networks for breast cancer classification. This technique will make these pre-trained networks more suitable for medical image classification purposes. More importantly, DSTL can help speed up convergence significantly. 2. DSTL can update the learnable parameters (weights and biases) of any pre-trained network by fine-tuning them on a large dataset that is similar, but not identical, to the target dataset. The proposed DSTL adds new instances (CBIS-DDSM) to the source domain (\(\mathcal{D}_{s}\)) that are similar to the target domain (\(\mathcal{D}_{t}\)) to update the weights of the parameters in the pre-trained models and form a distribution similar to the \(\mathcal{D}_{t}\) (MIAS and BCDR datasets). 3. The number of X-ray images is enlarged by a combination of effective augmentation methods that are carefully chosen based on the most common image display functions performed by doctors and radiologists during the diagnostic image viewing. These augmentation methods include different variations of rotation, brightness, flipping, and contrast. These methods will reduce overfitting and produce robust results. 4. The proposed DSTL will provide a valuable solution to the difference between the source and target domain problem in transfer learning.