# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 2 Related Works
Several residual nets were used in [46] to classify breast density, including ResNet43, ResNet50, and ResNet101. The models applied on a clinical dataset consisted of 1985 mammograms, in addition to the INbreast dataset. During the preprocessing stage, images were resized to \(224\times 224\), the background was removed, and augmentation was applied. During the training stage, 5-fold cross validation was applied. The best performance was obtained from ResNet50, with an overall accuracy of 87.1% for the clinical dataset and 70% for INbreast. In [47], bilateral adaptive spatial and channel attention network (BASCNet) was used to classify breast density. The backbone of this model was ResNet. The training dataset that was used is a subset of DDSM consisting of 250 cases of each ACR class to handle the class imbalance problem. Various preprocessing stages were applied, including removing the background, image resizing to \(224\times 224\), and augmentation. In addition, 5-fold cross validation was applied during the training process. The testing was performed on both the DDSM and INbreast datasets, and the overall accuracy was 85.10% and 90.51%, respectively. Table 3, summarizes the deep learning classification techniques mentioned above. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Work Title** & **Dataset** & **Included Cases** & **Pre-Processing** & **Data Splitting** & **Deep Learning** & **Two Classes** & **ACR** & **AUC** \\  & & **Cases** & & **Techniques** & **Model** & **Accuracy** & **Accuracy** & \\ \hline \hline Deep & 200,000 & All & & & 80\% training, & Baseline & 81.1\% & 67.9\% & 0.832 \\ CNN [52] & mammograms & cases & Augmentation & 10\% testing, & 10\% validation & CNN & 86.5\% & 76.7\% & 0.916 \\ \hline Deep & MIAS & All & 1-Segment & 80\% training, & & & & \\ Learning & & & & pectoral & (apply) & & & \\ from & & cases & muscle & fire-fold & CNN & — & 83.6\% & — \\ Small & & & & and breast & cross- & — & \\ Dataset & & & 2-Augmentation & validation & & & \\
[33] & & & 3-Resize images & 20\% testing, & & & \\ \hline DENSITY & 1602 & & & Augmentation & 70\% training & & & \\ QUANT- & & images & & 30\% & VGG16 & — & 79.6\% & — \\ FICATION [36] & & & & testing & & & \\ \hline Neural & & & 1-Remove & ten-fold & InceptionV4 & & 89.97\% & \\ Networks & 18,157 & All & & 2-Grayscale & & Inception-SE & & 92.17\% & — \\  & & & transformation & cross- & ResNeX50 & — & 89.64\% & — \\ based on & images & cases & 3-Augmentation & validation & ResNeX5E & & 91.57\% & — \\ SE-Attention [34] & & & 4+Normalized into a & & DenseNet212 & & 89.20\% & — \\  & & & Gauss distribution & & DenseNet-SE & & 91.79\% & — \\ \hline Density & 41,479 & All & & 41,479 & & & & \\ Assessment & mammograms & & Augmentation & train-images & ResNet-18 & 86.88\% & 76.78\% & — \\ Using Deep & & & & test-images & & & \\ Learning [37] & & & 1-Remove & & & & \\ \hline BI-RADS & 3813 & & 1-Remove & & & & \\ density & mammograms & & background & — & InceptionV3 & — & 83.33\% & — \\ categorization [35] & & — & 2-Augmentation & & & & \\ \hline Automated & & All & 1-Remove & 80\% training, & cGAN for & & & \\ Density & & & & pectoral & (holdout & segmentation & — \\ Segmentation & & & muscles & cross- & and & — & 98.75\% & — \\ and & & & 2-Resize images & validation) & CNN for & & \\ Classification [88] & & & 3-Augmentation & 20\% testing, & classification & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classifying breast density using deep learning.