# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 3 Materials and Methods
### ECA-Net Based on VGG16  
The proposed model is based on VGG16, which is one of the famous CNN architecture that won the ImageNet large-scale visual recognition challenge (ILSVR) competition in 2014. There are two big motivations behind using VGG16 as our backbone model for ALL classification. Table 3 provides a comprehensive overview of plain VGG16. To begin with, it extracts the features at a low level by utilizing a smaller kernel size and fewer layers as compared to its counterpart VGG19 network and other deep learning models. Instead of having a huge number of hyper-parameters, the network follows a simple design, where each block of VGG16 consists of convolutional layers with 3 \(\times\) 3 filters and unit stride. Max pooling layers of 2 \(\times\) 2 windows with the stride of 2 is used to reduce the size of the image to half. Throughout the network, each block follows the same combinations of max pooling and convolution layers. In the end, it uses 3 fully connected layers (FC) for the output. The stacking of convolutional layers enables the network to capture more information with lesser computational overhead hierarchically. The newer deep learning models such as DenseNet [47], ResNet [48], Inception [49], etc. use a lot of convolution layers, whereas our objective was to create a simple deep learning model for ALL classification. Secondly, the VGG16 model has a higher feature extraction ability for the classification of ALL cell images as shown in [50]. The shallow network keeps more information about the underlying features, which is important for cell texture identification. The overall architecture of proposed model is shown in Figure 4. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Cancer Cell Images**} \\ \hline
**Fold** & **Before Augmentation** & **After Augmentation** \\ \hline
1 & 1119 & 2238 \\
2 & 1100 & 2200 \\
3 & 1200 & 2400 \\
4 & 1248 & 2436 \\
5 & 1203 & 2406 \\
6 & 1209 & 2418 \\ \hline Total & 7049 & 14,098 \\ \hline \multicolumn{3}{c}{**Normal Cell Images**} \\ \hline
**Fold** & **Before Augmentation** & **After Augmentation** \\ \hline
1 & 562 & 2248 \\
2 & 473 & 1892 \\
3 & 538 & 2152 \\
4 & 541 & 2164 \\
5 & 524 & 2096 \\
6 & 538 & 2152 \\ \hline Total & 3176 & 12,704 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset distribution before and after data augmentation for each fold. To explore high value featured related to ALL in the input image, we use an attention module to force the network to learn the high-level features. Attention not only illustrates where it should be focused, but also enhances the representation of features. [PERSON] et al. [52] proposed a local cross-channel interaction strategy which is realized via 1D convolution called ECA, which can be widely used to boost the representation power of CNNs. In addition, considering the complexity of ALL features, it is difficult for the traditional CNNs to train these images. VGG16 with an attention module is more suitable for the proposed problem due to the improvement of feature representation. The shallow convolution modules can only extract the edge and texture features. The deep convolution modules can provide more abstract semantic features, which can better distinguish between ALL cancer and healthy cell images. The ECA module is added to enhance and amplify the difference of semantic features extracted by VGG16. This model is expected to overcome the morphological similarity and further improve the classification performance. Moreover, the ECA module also allows the model to focus on more important channel features. The salient feature information is used by ECA attention to accomplish task adaptive feature pooling operation. The ECA module aids in medical image analysis to automatically learn to focus on target structures of various shapes and sizes. Intuitively, a model trained using the ECA module learns to suppress irrelevant regions in input images while emphasizing silent features for a given task to improve the accuracy and efficiency of a deep learning model.