# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 2 Related Works  
Recently, many studies have sought to develop deep learning models to assess breast density. Some of these studies target two classes of density (fatty or dense), while other studies classify the breast as fatty, glandular, or dense. However, most studies classify breast density into four classes according to the BI-RADS system. Here we mention only recent works related to ACR classification, as it is a standard in medical reporting. In [32], the authors proposed a breast density classification model based on convolutional neural networks (CNNs). They applied two techniques to 200,000 breast screenings. They called the first technique a baseline and the second a deep convolutional neural network (deep CNN). In the baseline, they used pixel intensity histograms of screening as input features. Then, softmax regression was used as a classifier. In deep CNN, the inputs were the four screening views, while the fully connected layer consisted of 1024 hidden units and the output layer used the softmax activation function. Additionally, they used the weights of the previously trained model of breast cancer detection to initialize the parameters of their network. Both techniques were measured by computing the area under the ROC curve (AUC), the accuracy of super-classes (dense or non-dense), and the ACR accuracy. For the baseline with 20 bins, AUC = 0.832, ACR accuracy = 67.9%, and super-classes \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**CNN Model** & **Model Name Origin** & **Number of Layers** & **Top-1 Accuracy** & **Year Established/Updated** \\ \hline VGG16 & Visual Geometry Group & 16 & 71.3\% & 2015 \\ \hline VGG19 & Visual Geometry Group & 19 & 71.3\% & 2015 \\ \hline ResNet50V2 & Residual Neural Network & 103 & 76.0\% & 2016 \\ \hline InceptionV3 & Inception & 189 & 77.9\% & 2016 \\ \hline Xception & Extrem Inception & 81 & 79\% & 2017 \\ \hline InceptionResNetV2 & Inception-Residual Neural Network & 449 & 80.3\% & 2017 \\ \hline DenseNet121 &
\begin{tabular}{l} Densely Connected \\ Convolutional Networks \\ \end{tabular} & 242 & 75.0\% & 2017 \\ \hline MobileNetV2 & MobileNet & 105 & 71.3\% & 2018 \\ \hline EfficientNetB0 & EfficientNet & 132 & 77.1\% & 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pre-trained CNNs. accuracy = 81.1%. For deep CNN, AUC = 0.916, ACR accuracy = 76.7%, and super-classes accuracy = 86.5%. Another CNN was then applied to the MAIS dataset to classify breast density in [33]. Different preprocessing techniques were used, including pectoral muscle segmentation, image augmentation, and image resizing. The CNN consists of three convolutional layers, followed by two fully connected layers, and, finally, the output layer. The dataset was divided, with 20% used for testing and 80% for training, before five-fold cross-validation was applied. The overall accuracy of ACR classification was 83.6%. Moreover, in [34] the CNNs were applied with a squeeze-and-excitation network (SE-Net) mechanism to classify breast density from mammograms. The three CNN models used with SE-Net were Inception-V4, ResNeXt, and DenseNet. A 10-fold cross-validation was used to obtain better results. The dataset consisted of 18,157 images. The preprocessing entailed removing the background, grayscale transformation, augmentation by cropping and rotating images, and normalizing the images into a normal distribution. The classification accuracy was measured for each model with and without SE-Attention. The accuracy of Inception-V4 and Inception-V4-SE-Attention was 89.97% and 92.17%, respectively, while for ResNeXt50 and ResNeXt50-SE-Attention, the accuracy was 89.64% and 91.57%, respectively. Finally, the accuracy of DenseNet121 was 89.20%, and for DenseNet121-SE-Attention it was 91.79%. Furthermore, in [35], a fine-tuned model based on InceptionV3 was used to classify breast density. The dataset consists of 3813 mammogram screenings. The accuracy obtained by the model for the BI-RADS classification based on 150 screenings was 83.33%.