# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 6 Conclusions  
In the study, three types of ultrasound imaging instruments including Model B, Model B, and Model C were adopted to acquire breast ultrasound images for our experimental samples. A series of image processing operations such as speckle noise removal, image normalization, and image enhancement etc. were performed to detect the breast lesion contours, extract features related to BI-RADS category with/without feature selection procedure depending on the selected classifier. Finally, the multi-class classifiers based on machine learning methods were implemented to identify actual BI-RADS 2-5 Categories in terms of the selected features. Experimental results also reveal that the proposed system not only can automatically, accurately, and reliably detect multi-tumors, but also show that the identification accuracies performed with SVM, RF, and CNN were evaluated as 80.00%, 77.78%, and 85.42%, respectively. CNN is higher in accuracy than RF and SVM. Because CNN model used is a pixel-based structure with feature map in extracting features whose features can be more than a total of 145 mass features that were extracted in our proposed feature selection. Using different ultrasound imaging instruments, evaluations of F-score based on CNN can obtain various measures higher than 75% when samples were tested on various BI-RADS categories.  
In the future, we hope to acquire sufficient BI-RADS 3-4 samples from ultrasound elastography and B-Mode ultrasound images in order to improve and validate the evaluation performance. Cross-validation can thus also be demonstrated if we can further combine with much more ultrasound samples and histopathological examination results so that the performance of the feature extraction and the classification can be further improved in the proposed CAD system.  
[PERSON] envisioned and coordinated the invited paper preparation; [PERSON] contributed to manuscript preparation, most of the experiments, and partial writing; [PERSON] and [PERSON] contributed to manuscript writing and paper revision including some of the experiments and experimental analysis. [PERSON] and [PERSON] supervised the process. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 105-2221-E-415-020-MY2. [PERSON] identified BI-RADS category on the selected breast ultrasound images.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **BI-RADS** & **Recall (\%)** & **Precision (\%)** & **F-score** & **Accuracy (\%)** \\ \hline \multirow{4}{*}{SVM} & 2 & 90.91\% & 83.33\% & 86.96\% & \multirow{4}{*}{80.00\%} \\  & 3 & 33.33\% & 50.00\% & 40.00\% & \\  & 4 & 28.57\% & 100.00\% & 44.44\% & \\  & 5 & 100.00\% & 76.47\% & 86.67\% & \\ \hline \multirow{4}{*}{RF} & 2 & 89.47\% & 94.44\% & **91.89**\% & \multirow{4}{*}{77.78\%} \\  & 3 & 60.00\% & 37.50\% & 46.15\% & \\  & 4 & 44.44\% & 57.14\% & 50.00\% & \\  & 5 & 91.67\% & 91.67\% & **91.67**\% & \\ \hline \multirow{4}{*}{CNN} & 2 & 94.12\% & 88.89\% & 91.43\% & \multirow{4}{*}{**85.42\%**} \\  & 3 & 77.78\% & 87.50\% & **82.35**\% & \\ \cline{1-1}  & 4 & 66.67\% & 85.71\% & **75.00**\% & \\ \cline{1-1}  & 5 & 92.31\% & 80.00\% & 85.71\% & \\ \hline \hline \end{tabular}
\end{table}
Table 9: A comparison of the performances of different machine learning classifiers.