# Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images
## 3 Experimental Results
### Evaluation Metrics  
In terms of classification performance, the two datasets analyzed in Section 3.1 are split in 60-40% train-validation ratio for the colon cancer dataset and 70-30% for the breast cancer dataset. Although the 60-40% split in the first case is considered rather strict, this choice supports the purposes of this study concerning the trade-off between performance and explainability. Single EfficientNets achieve accuracy near perfection for the colon cancer dataset. The choice of split (60-40%) manages to lower the accuracy metric of single EfficientNets and, therefore, demonstrate the improvement in performance  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Samples** & **Percentage (\%)** \\ \hline ADI & 10,407 & 10.4 \\ BACK & 10,566 & 10.56 \\ DEB & 11,513 & 11.51 \\ LYM & 11,556 & 11.56 \\ MUC & 8896 & 8.9 \\ MUS & 13,537 & 13.54 \\ STR & 8763 & 8.76 \\ NORM & 10,446 & 10.45 \\ TUM & 14,316 & 14.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class distribution of the colorectal dataset.  
Figure 5: This is an overview of colon cancer dataset. Each image depicts a specific tissue type.: adipose is indicated as (ADI), background as (BACK), debris as (DEB), and lymphocytes as (LYM), mucus as (MUC), smooth muscle as (MUS), normal colon mucosa as (NORM), cancer associated stroma as (STROMA), and colorectal adenocarcinoma epithelium as (TUM).  
when utilizing ensemble classifiers. The utilized performance metrics for the binary and multiclass classification tasks are described hereafter:  
* Accuracy metric is defined as the fraction of the correctly classified instances divided by the total number of instances, as shown in Equation (5): \[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN})\] (5)  
Correctly classified instances are analyzed in true positives (TP) and true negatives (TN), where TP are the instances predicted as positive and truly are positives (ground truth) and TN are the instances predicted as negative and truly are negative. The total number of instances consists of TP, TN, the false positive (FP), and false negative (FN) instances. FP are the instances that are predicted as positive by the classifier but are negative in reality, whereas FN are the instances predicted as negative but are positive;  
* Precision metric is defined as the fraction of the true positives divided by the true positives and false positives as shown in Equation (6): \[\text{Precision}=\text{TP}/(\text{TP}+\text{FP})\] (6)
* Recall metric is defined as the fraction of the true positives divided by the true positives and false negatives as shown in Equation (7): \[\text{Recall}=\text{TP}/(\text{TP}+\text{FN})\] (7)
* Area under Curve (AUC) metric is defined as the area under the receiver operating curve. The receiver operating curve is drawn by plotting true positive rate (TPR) versus false positive rate (FPR) at different classification thresholds. TPR is another word for recall, whereas FPR is the fraction of the false positives divided by the true negatives and false positives as shown in Equation (8): \[\text{FPR}=\text{FP}/(\text{TN}+\text{FP})\] (8)  
Although balanced accuracy is the appropriate performance metric when dealing with imbalanced datasets such as BreakHis, accuracy is chosen in order to provide comparison feedback in reference to the state of the art. In terms of measuring the performance of the explanation scheme, an evaluation tool runs on for specialists to test and review the results of explanation schemes. The results of this evaluation are reported in the following section.