## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Method** & \begin{tabular}{c} **Classification** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Type of** \\ **Feature** \\ **Extraction** \\ \end{tabular} & **Accuracy (\%)** & \begin{tabular}{c} **F1-Score** \\ **Measure (\%)** \\ \end{tabular} & **Specificity (\%)** & **AUC (\%)** \\ \hline Al-Haija and [PERSON] (2020) [33] & ResNet-50 CNN & Binary & \begin{tabular}{c} Automatic \\ unsupervised \\ feature \\ discovery \\ \end{tabular} & 99 & n/a & n/a & n/a \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al. \\ (2019) [34] \\ \end{tabular} } & VGG19, & \multirow{4}{*}{Binary} & Automatic & \multirow{4}{*}{98.13} & \multirow{4}{*}{98.64} & \multirow{4}{*}{n/a} & \multirow{4}{*}{n/a} \\  & MobileNet, & & & & & \\ \cline{1-1}  & DenseNet and & & & & & \\ \cline{1-1}  & multi-layer & & & extraction & & & \\ \cline{1-1}  & perceptron & classifier & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. \\ (2022) [26] \\ \end{tabular} } & \multirow{2}{*}{6B-Net} & \multirow{2}{*}{4-class} & Automatic & \multirow{2}{*}{94.20 \(\mid\)90.10} & \multirow{2}{*}{n/a} & \multirow{2}{*}{n/a} & \multirow{2}{*}{n/a} & \multirow{2}{*}{n/a} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON]. [PERSON] et al. \\ (2017) [27] \\ \end{tabular} } & CSDCNN & End-to-end & 8-class & n/a & 93.2 & n/a & n/a & n/a \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] et al. \\ al. (2018) [30] \\ \end{tabular} } & Variant & \multirow{4}{*}{4-class} & Deep Learning for Feature Extraction & \multirow{4}{*}{96.25} & \multirow{4}{*}{n/a} & \multirow{4}{*}{n/a} & \multirow{4}{*}{n/a} \\  & ResNets & & & & & \\ \cline{1-1}  & models & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} [PERSON] et al. \\ (2019) [28] \\ \end{tabular} } & \multirow{2}{*}{CNN end- end} & \multirow{2}{*}{4-class} & Deep learning & \multirow{2}{*}{92.45 \(\mid\)95.48} & \multirow{2}{*}{n/a} & \multirow{2}{*}{91.11 \(\mid\)96.97} & \multirow{2}{*}{n/a} \\  & & & & & extraction & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Proposed** \\ **MPIFR +** \\ **SVM Model** \\ \end{tabular} } & Four variant & \multirow{2}{*}{8-class} & Automatic & \multirow{2}{*}{**97.77**} & \multirow{2}{*}{**97.92**} & \multirow{2}{*}{**99.57**} & \multirow{2}{*}{**99.00**} \\  & CNN \& SVM & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Prior models for 2- and 4-class BC classification including performance comparisons. Our proposed approach (in bold) outperforms prior approaches. Novelty: Our work is novel because while some prior work has utilized CNNs for 2- and 4-class breast cancer classification, they did not explore using a multi-scale pooled image feature representation (MPIFR) to classify histopathological images into eight (8) BC classes. Specifically, some prior work utilized CNNs for the classification of four (4) BC classes using 6B-Net with deep feature fusion and achieved an accuracy of 94.2%. Innovatively, the proposed ensemble approach leverages several key insights. First, pre-training state-of-the-art DCNNs on huge repositories such as the 14 million image ImageNet repository provides them with the intelligence to learn low-level features such as edges and corners from images of histological breast cancer. Secondly, by extracting features from multiple resolutions of histopathological images, classification accuracy is improved due to the fact that specific visual characteristics may be more visible at different resolutions. Thirdly, the extraction of multiresolution breast cancer features creates a powerful set of features that can be classified using SVM for highly accurate multi-class classification of histopathological images of breast cancer.