# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 5 Conclusions  
In this research, an effective transfer learning technique called double-shot transfer learning (DSTL) has been introduced to improve the overall accuracy and performance of various pre-trained models, especially in the field of medical image analysis. Simple and effective image augmentation techniques were also used to overcome the lack of breast X-ray images, improve invariance, and reduce overfitting by generating new training samples based on the existing breast X-ray images manipulations. The most common image display functions performed by doctors and radiologists during the diagnostic image viewing have been considered as the augmentation methods for this research. The proposed technique will overcome the lack of available training samples issues, improve the pre-trained models accuracy and performance, and will provide a valuable solution to the difference between the source and target domain problem in transfer learning.  
Conceptualization, Y.-L.C. and M.A.; methodology, Y.-L.C. and M.A.; software, M.A. and P.K.C.; validation, Y.-L.C., S.-C.M. and B.H.; formal analysis, Y.-L.C. and B.H.; investigation, M.A.; data curation, M.A., P.K.C. and V.P.A.; writing--review and editing, M.A. and Y.-L.C.; supervision, Y.-L.C. and S.-C.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially sponsored by the Ministry of Science and Technology, Taiwan, Grant Nos. MOST 108A27A, 108-2116-M-027-003, and 107-2116-M-027-003, National Space Organization, Taiwan, Grant No. NSPO-S-108216, Sinotech Engineering Consultants Inc., Grant No.A-RD-I7001-002, and National Taipei University of Technology, Grant Nos. USTP-NTUT-NTOU-107-02, NTUT-USTB-108-02 and NTUT-UM-109-01.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} VGG & Visual Geometry Group \\ ResNet & Residual Network \\ SSTL & Single-Shot Transfer Learning \\ DSTL & Double-Shot Transfer Learning \\ CAD & Computer-Aided Diagnosis \\ CNN & Convolutional Neural Network \\ DDSM & Digital Database Screening Mammography \\ CBIS-DDSM & Curated Breast Imaging Subset of DDSM \\ MIAS & Mammographic Image Analysis Society \\ BCDR & Breast Cancer Digital Repository \\ ILSVRC & ImageNet Large-Scale Visual Recognition Challenge \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset \& Technique** & **Model** & **Training-Time** & **Epoch** & **Iteration** \\ \hline \multirow{8}{*}{BCDR (With SSTL)} & AlexNet & 02 min 45 s & 42 & 1974 \\  & ShuffleNet & 10 min 30 s & 30 & 1410 \\  & MobileNet-v2 & 09 min 51 s & 23 & 1081 \\ \cline{1-1}  & GoogleLeNet & 05 min 51 s & 28 & 1316 \\ \cline{1-1}  & ResNet-50 & 21 min 37 s & 42 & 1974 \\ \cline{1-1}  & ResNet-101 & 13 min 38 s & 14 & 658 \\ \cline{1-1}  & VGG-16 & 11 min 56 s & 21 & 987 \\ \cline{1-1}  & VGG-19 & 28 min 11 s & 43 & 2021 \\ \hline \multirow{8}{*}{BCDR (With DSTL)} & AlexNet+ & **02 min 40 s** & **41** & **1927** \\  & **ShuffleNet+** & **05 min 02 s** & **16** & 752 \\ \cline{1-1}  & **MobileNet-v2+** & 07 min 13 s & **17** & 799 \\ \cline{1-1}  & **GoogLeNet+** & **03 min 32 s** & **17** & 799 \\ \cline{1-1}  & **ResNet-50+** & **15 min 13 s** & **30** & **1410** \\ \cline{1-1}  & **ResNet-101+** & **11 min 04 s** & **11** & 517 \\ \cline{1-1}  & **VGG-16+** & **09 min 30 s** & **15** & **705** \\ \cline{1-1}  & **VGG-19+** & **08 min 27 s** & **13** & **611** \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._