# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 3 Experimental Results
### Results
The images are selected randomly from the validation set of BreakHis dataset and the Bachs dataset [52] and processed by both Grad-CAM and Guided Grad-CAM explainability techniques. The visualization and classification results are analyzed by specialized personnel and commented on terms of their opinion concerning the classification in benign or malignant class and the localization of important visual patterns that are responsible for the classification result. In Figure 7, a benign adenosis is depicted in \(\times\)400 magnification. The ensemble classifier classifies the image as probably benign but not being totally representative with high confidence in contrast to the experienced physician that refers to this image as not being totally representative of the benign class in terms of morphological patterns. The red highlighted regions are localized on epithelial tissue, though not totally. Humans tend to point their attention on the specific kind of tissue because carcinomas are malignant neoplasms of epithelial tissue. On the other hand, nearby stromal and epithelial areas are colored with yellow as they are in the vicinity of the most important regions. Concerning the Guided Grad-CAM algorithm, the coloring of respective areas is fuzzier but still more intense on the epithelial patterns. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{6}{c}{**Breast Cancer**} & \\ \hline
**Metrics** & **Acc** & **Pr** & **Rec** & **Acc** & **Pr** & **Rec** & **Acc** & **Pr** & **Rec** & **Acc** & **Pr** & **Rec** \\ \hline
**Magnification Factor** & & **40\(\times\)** & & & **100\(\times\)** & & & **200\(\times\)** & & & **400\(\times\)** & \\ \hline
**Architecture** & & & & & & & & & & & & \\ \hline EfficientNetB0 & 0.9097 & 0.9215 & 0.9030 & 0.8702 & 0.8849 & 0.8622 & 0.8313 & 0.8569 & 0.8207 & 0.8333 & 0.8552 & 0.8114 \\ EfficientNetB1 & 0.8796 & 0.8948 & 0.8679 & 0.8638 & 0.8696 & 0.8446 & 0.8313 & 0.8569 & 0.8207 & 0.8205 & 0.8340 & 0.8004 \\ EfficientNet B2 & 0.8796 & 0.8948 & 0.8979 & 0.8798 & 0.8918 & 0.8718 & 0.8629 & 0.8723 & 0.8401 & 0.8040 & 0.8275 & 0.7729 \\ EfficientNet B3 & 0.8963 & 0.9103 & 0.8829 & 0.8846 & 0.8893 & 0.8750 & 0.8594 & 0.8703 & 0.8489 & 0.8443 & 0.8681 & 0.8351 \\ EfficientNetB0-2 & 0.9114 & 0.9248 & 0.9047 & 0.8686 & 0.8744 & 0.8590 & 0.8629 & 0.8915 & 0.8524 & 0.8443 & 0.8641 & 0.8150 \\ EfficientNetB1-3 & 0.9264 & 0.9368 & 0.9164 & 0.8963 & 0.9103 & 0.8829 & 0.8664 & 0.8775 & 0.8436 & 0.8571 & 0.8716 & 0.8333 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance metrics [Accuracy(Acc), Precision (Pr) and Recall(Rec)] for the breast dataset for selected baseline and ensemble architectures at different magnification factors (40\(\times\), 100\(\times\), 200\(\times\), 400\(\times\)) concerning the multiclass classification task. Moving on to the next image presented in Figure 8 which is taken from the Bachs dataset and depicts an in situ carcinoma, the depicted patterns are visually representative of the malignant class. The classifier correctly predicts the class with high confidence and manages to generalize well on an unknown dataset with several variances owing to different production and staining procedures. Concerning the Grad-CAM technique, highly important regions colored as red correspond to epithelial cells, whereas, in the Guided Grad-CAM case, the coloring of respective regions is fuzzy. Some yellow painted regions are considered of less importance to the classifier and highlighted due to the vicinity to the most important regions and other yellow regions are colored with no obvious reason to experienced physicians. Figure 6: Overview of the standalone application for the classification and explanation of histopathology images. Figure 7: Application of (**b**) Grad-CAM and (**c**) Guided Grad-CAM explainability techniques on (**a**) a benign adenosis sample from the BreakHis dataset. In other cases, both algorithms fail to highlight the regions which are considered significant by experienced physicians.