# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 1 Introduction  
Recently, various machine learning algorithms have been used to develop computer-aided diagnosis (CAD) systems to enhance the diagnostic capabilities of breast cancer in medical images. These algorithms are mainly based on traditional classifiers that rely on hand-crafted features in order to solve a particular machine learning task. Therefore, these kinds of methods are considered to be tedious, time-consuming, and require experts in the field, especially in the feature extraction and selection tasks [1]. Recent studies have shown that deep learning methods can produce promising results on tasks such as image classification, detection, and segmentation in different fields of computer vision and image processing. Training these deep learning algorithms from scratch to produce accurate results and avoid overfitting remain an issue due to the lack of medical images available for experiments [2]. In recent years, some techniques such as transfer learning and image augmentation have shown promising opportunities towards increasing the number of training data, overcoming overfitting, and producing robust results [3]. There are some interesting studies about breast cancerdetection and classification by using deep learning methods along with other techniques such as image augmentation and transfer learning in different types of medical images. [PERSON] and [PERSON] [4] presented how convolutional neural networks (CNNs) and pre-trained models such as AlexNet and GoogLeNet can be used to classify pre-segmented breast masses as benign or malignant in X-ray images, using a combination of transfer learning and data augmentation techniques to overcome the limited training data. Nevertheless, the authors have only tested two pre-trained networks on one dataset known as a digital database for screening mammography (DDSM). Therefore the experiments might be insufficient to generalize the findings of their study. Another research [5] showed that image augmentation is a vital part of training discriminative CNNs and presented some augmentation methods such as horizontal flips, random crops, and principal component analysis that have been used to capture important characteristics of medical image statistics effectively resulting in high validation and training accuracy. In addition, they demonstrated that smarter augmentation may result in fewer artifacts in CNN visualizations. However, the augmentation methods seem to be randomly chosen and not based on expert knowledge or experience. One recent research [6] introduced transfer learning and image augmentation methods to construct an automatic mammography classification using the public dataset of curated breast imaging subset of DDSM (CBIS-DDSM). Residual network (ResNet) has been fine-tuned in order to produce good performance, decrease training time, and automatically extract features. Although the overall accuracy of the proposed method reached 93.15%, the result remains doubtful since authors tested the proposed approach on the augmented dataset rather than the original dataset. Another recent research [7] has experienced transfer learning on recent pre-trained models to evaluate their performance on benign and malignant breast cancer classification in mammograms. The region of interest (ROI) mass images from the public dataset of CBIS- DDSM have been used for training and testing. The best results were obtained with ResNet-50 and MobileNet with 78.4% and 74.3%, respectively. Nevertheless, the accuracy is considered to be very low. [PERSON] et al. [8] presented a breast imaging CAD system based on transfer learning from non-medical tasks to extract lesion information from breast mammographic images which contain 219 breast lesions. Authors demonstrated the effectiveness of the proposed approach compared to the traditional classifier of support vector machine based on CNN as a feature extractor. Although the proposed method has improved the classification accuracy, it might suffer from overfitting due to the small number of training samples. [PERSON] et al.