# A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks
## 2 Literature Review  
In recent years, many algorithms were introduced to help in classifying histopathology images. Nowadays, CNNs are considered as the state-of-the-art algorithm for classifying images. In 1989, [PERSON] et al. [19] presented the first CNN with 5 convolution layers. Before [PERSON]'s work, CNN was not a popular choice for image classification because of the computational cost and the (small) size of the available datasets. In 2012, [PERSON] et al. [7] re-introduced CNNs by winning the ImageNet challenge with their AlexNet CNN that obtained a 16% classification error rate compared to 25% of the second-place model. Since then, CNN became the de-facto algorithm for image classification, and many CNNs were subsequently defined.  
To address an image classification task by using CNNs, it is possible to rely on two different approaches: use a pre-existing architecture that was developed to classify natural images or, as done in this paper, develop a novel architecture. This section focuses on novel architectures that were introduced mainly to classify histopathology images. We summarize the recent contributions in the area of histopathology images in Table 1, while Table 2 reports the important information associated with the architectures presented in the papers of Table 1. For a complete description of the architectures considered in Tables 1 and 2, the reader is referred to their respective paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & \multicolumn{2}{c}{**Main Contribution**} & \begin{tabular}{c} **Number of** \\ **Classes** \\ \end{tabular} & \
\begin{tabular}{c} **Metric** \\ **Performance** \\ \end{tabular} &
\begin{tabular}{c} **Test Set** \\ **Performance** \\ \end{tabular} \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & \(\bullet\) & A CNN is used to classify breast & & & \\  & cancer images belonging to 8 classes & & & & \\  & & (four benign subclasses and four & & & \\  & malignant subclasses). & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & \(\bullet\) & Fully automated diagnostic tool for & & & \\  & non-alcoholic fatty liver disease & & & & \\  & classification, based on an optimized & 2 & Accuracy & 95\% \\ \cline{1-1}  & & CNN architecture. & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & \(\bullet\) & Deep learning model for nucleus & & & 0.692 (This \\  & detection and classification from & & & & is the \\  & histology images of & & & & combined \\  & colorectal adenocarcinoma. & & & & performance \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & \(\bullet\) & Deep learning model that integrates & & & & \\  & Coding Network with Multilayer & & & & 90.1\% and \\  & & Perceptron (CNMP). & & & 90.2\% on \\  & & Combination of high-level features & & & two \\  & & that are extracted from a deep & 2 & Accuracy & benchmark \\  & & convolutional neural network with & & & medical \\  & & traditional features of an image that & & & image \\  & & & & & datasets \\  & & & & & \\  & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [25]} & \(\bullet\) & Definition of a CNN architecture for & & & \\  & the classification of histological & & & & \\  & & routine colon cancer nuclei. & & & \\ \cline{1-1}  & & & &  
To assess the performance of the model proposed in this paper, we perform a comparison against the performance of three state-of-the-art models, namely, VGG, InceptionV3, and ResNet architectures. The following paragraphs provide a short description of these models. For a complete description, the reader is referred to the papers where these models were originally presented.