# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 3 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Breast Cancer**} & \multicolumn{2}{c}{**Colon Cancer**} \\ \hline
**Architecture** & **Accuracy** & **AUC** & **Accuracy** & **AUC** \\ \hline EfficientNetB0 & 0.9766 & 0.9945 & 0.9946 & 0.9993 \\ EfficientNetB1 & 0.9798 & 0.9964 & 0.9898 & 0.9984 \\ EfficientNet B2 & 0.9817 & 0.9982 & 0.9920 & 0.9988 \\ EfficientNet B3 & 0.9855 & 0.9988 & 0.9897 & 0.9984 \\ EfficientNet B4 & 0.9858 & 0.9980 & 0.9910 & 0.9982 \\ EfficientNet B5 & 0.9804 & 0.9975 & 0.9924 & 0.9982 \\ EfficientNet B6 & 0.9728 & 0.9953 & 0.9894 & 0.9986 \\ ExceptionNet & 0.9785 & 0.9942 & 0.9909 & 0.9985 \\ InceptionNetV3 & 0.8868 & 0.9430 & 0.9844 & 0.9981 \\ VGG16 & 0.9320 & 0.9769 & 0.9795 & 0.9969 \\ ResNet152V2 & 0.8720 & 0.9431 & 0.9564 & 0.9913 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for the breast and colon cancer dataset for baseline architectures. Baseline architectures leave small space for improvement in performance; even when splitting the dataset in 60-40%, the ensemble architecture managed a minor improvement in some cases. Nevertheless, in the worst-case scenario, the proposed ensemble architectures are on par with the baseline implementations. The task of classification is made more difficult by splitting the dataset 40-60% (training-validation) and 30-70%. Each experiment is conducted by splitting the dataset into two subsets at the beginning of the study to avoid introducing bias. Consequently, each validation process is conducted without receiving any information about the images used for training. Bootstrapping the splits 10 times is performed to enhance randomness. In Table 7, the results from these two extreme splits are demonstrated. The difference in performance metrics is not significant even as the problem of classification becomes more difficult. Returning to the BreakHis dataset, four datasets are generated by the partition of the initial dataset to subsets based on the magnification factor. The four datasets correspond to the magnification factors 40\(\times\), 100\(\times\), 200\(\times\), 400\(\times\). Two classification tasks are addressed depending on the assigned labels. The first classification task is binary where the classes are benign and malignant, whereas the second classification task is multiclass where the classes are adenoma, fibroadenoma, tubular adenoma, phylllodes tumor, ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The training-validation split is set to 70-30%. As shown in Tables 8 and 9, ensemble classifiers achieve better performance at all magnification factors in both tasks apart from one binary classification case at 100\(\times\), where classifiers perform equally. Regarding the explainability task of the proposed methodology, a test bench application was developed for visual inspection and verification of the produced results by specialized medical personnel. The web interface (Figure 6) is available in the URL http: 83.212.75.102:3005/ (accessed on 23 September 2021) and upon uploading of a histopathology image, the sample is sent to the back end where the best performing ensemble architecture returns the classification result along with the generation of a heatmap of the original image. The visual patterns of the image that are characterized as highly related to the result are painted red, whereas those irrelevant with blue. The explainability capability of the different deep frameworks or ensemble classifiers are evaluated on a quality basis by expert pathologists in the respective field. The specialists inspect the highly related visual patterns and assess the results according to their prior experience in histopathology image-based diagnosis. The initial qualitive results show significant accordance concerning the areas responsible for the characterization of results between specialists and the ensemble classifier.