## 5 Results
Columns correspond to targeted classes and rows to output classes. Diagonal cells correspond to correctly classified observations. Off-diagonal cells are referred to as incorrect classifications. There is also a percentage of the overall number of observations and a number of observations for each cell. On the extreme right, you can see the proportion of incorrectly predicted classifications (red color) and correctly predicted classifications (green color). In statistics, these metrics are called false discovery rate and positive predictive value. The lowest row indicates the percentage of incorrectly classified and correctly classified results, referred to as false negative rate (FNR) and true positive rate (TPR). In the bottom-most right cell, you can see the general precision. Column-normalized column summaries show the percentage of correctly classified observations for every predicted class. Using row-standardized row summaries, you can see how many observations are incorrectly classified and how many are correctly classified. As can be seen in the confusion matrix, the majority of results fall on the leading diagonal with very few off the diagonal, demonstrating that the proposed approach did not confuse benign and malignant cells. \begin{table}
\begin{tabular}{c c c} \hline
**Nodes** & **Accuracy (\%)** & **Time (Hours)** \\ \hline Densenet201\_Resnet50 & 0.9684 & 39.87 \\ \hline Densenet201\_Efficientnetb0 & 0.9562 & 42.99 \\ \hline Densenet201\_Resnet18 & 0.9617 & 36.28 \\ \hline Resnet18\_Resnet50 & 0.9633 & 8.23 \\ \hline Resnet50\_Efficientnetb0 & 0.9676 & 14.8 \\ \hline Resnet18\_Efficientnetb0 & 0.9587 & 11.225 \\ \hline Resnet18\_Efficientnetb0\_Densenet & 0.9676 & 45.17 \\ \hline Resnet18\_Efficientnetb0\_Resnet50 & 0.9730 & 17.12 \\ \hline Efficientnetb0\_Densenet\_Resnet50 & 0.9739 & 48.74 \\ \hline Resnet50\_Densenet\_Resnet18 & 0.9735 & 42.18 \\ \hline
**Ensemble model** & **0.9777** & 50.79 \\ \hline \end{tabular}
\end{table} 
Table 9: Performance of the different combination of baseline models. The MPIFR model classified all eight cancer subtypes with 97.77% accuracy and 99.57% specificity on average. This means that for a specific subtype, the individual classifier in our proposed model has a high ability to discriminate one class of BC subtype from another. The classifier SVM performance was evaluated with ten-fold cross-validation with a cross-validation error of 0.0432. This is a good indication of the significance and consistency of the results of classifiers of the corresponding individual cancer subtypes. Further, to demonstrate that the difference in performance between the MPIFR and other ensemble baselines was statistically significant, the Nemenyi post hoc test [55] was performed. At a confidence level of a = 0.05, the critical distance (CD) is 1.2536. Our model F1-score is 97.92%. The F1-score is the harmonic average of precision and sensitivity. While precision measures the extent of the error caused by false positives and sensitivity measures the extent of the error caused by the false negative. Figure 18 is a T-SNE plot that illustrates that our proposed MPIFR method adequately discriminates between the eight target BC classes in feature space. Figure 17: Confusion matrix displaying the performance of the proposed ensemble approach. AUC is an effective way to summarize the overall accuracy of the test. In general, an AUC of 0.5 suggests no discrimination (i.e., the ability to diagnose patients with or without the BC based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. The MPIFR has an AUC of 0.99 (99%). Table 10 presents detailed results of the performance of the ensemble model.