## 6 Discussion  
As shown in Table 10, in rigorous evaluation experiments, the proposed MPIFR outperformed a comprehensive set of baseline models and also previous state-of-the-art techniques for both binary and multiclassification (Table 1) of histopathological images. Our results also demonstrate that all key components of our approach contribute non-trivially to its superior results, including: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Disease** & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F1-Score** & **AUC (\%)** \\ \hline Adenosis & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline Ductal & 98.7 & 96.43 & 98.71 & 97.28 & 97.56 & 98.00 \\ Carcinoma & 99.3 & 99.67 & 99.34 & 99.95 & 99.51 & 99.65 \\ \hline Lobular & \multirow{2}{*}{83.5} & \multirow{2}{*}{94.15} & \multirow{2}{*}{83.49} & \multirow{2}{*}{99.49} & \multirow{2}{*}{88.50} & \multirow{2}{*}{91.49} \\ Carcinoma & & & & & & \\ \hline Mucinous & \multirow{2}{*}{99.6} & \multirow{2}{*}{100} & \multirow{2}{*}{99.58} & \multirow{2}{*}{100} & \multirow{2}{*}{99.79} & \multirow{2}{*}{99.79} \\ Carcinoma & & & & & & \\ \hline Papillary & \multirow{2}{*}{99.4} & \multirow{2}{*}{98.81} & \multirow{2}{*}{99.40} & \multirow{2}{*}{99.91} & \multirow{2}{*}{99.10} & \multirow{2}{*}{99.66} \\ Carcinoma & & & & & & \\ \hline Phylodes & \multirow{2}{*}{99.3} & \multirow{2}{*}{98.53} & \multirow{2}{*}{99.26} & \multirow{2}{*}{99.91} & \multirow{2}{*}{98.89} & \multirow{2}{*}{99.58} \\ Tumor & & & & & & \\ \hline Tubular & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} & \multirow{2}{*}{100} \\ Adenoma & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of ensemble model for each of the eight classes. Figure 18: T-SNE visualization to illustrate that our proposed MPIFR method adequately discriminates between the eight target BC classes in feature space. Transfer learning by pre-training on a large image repository (ImageNet) with fine-tuning on the BreakHis breast cancer image dataset: that enables the CNN feature extractors models to learn a robust image representation from the large image repository. Fine-tuning on the BreakHis breast cancer dataset transfers the learned intelligence to the task of analyzing and classifying breast cancer. This conclusion is evident in Table 9.  
Using an ensemble of DCNNs for deep feature extractors: This step also facilitates downstream classification with traditional machine learning algorithms such as SVM. Deep MPIFRs are a powerful representation, which had the best performance for all combinations of the DCNN model explored in this study as shown in Table 7. The proposed technique of using MPIFR features, combined and classified using SVM outperformed single DCNN models approaches in Table 6. Compared with a single pre-trained CNN, it achieves superior performance for feature extraction (see Tables 6 and 8). While SVM is utilized for final classification, pre-trained CNNs were utilized for feature extraction. SVM, a classic machine learning algorithm was utilized for classification because the features extracted by the CNN are relatively small for each of the target classes. We also show in Table 8 that the four state-of-the-art CNN models (ResNet50, ResNet18, DenseNet201, and EfficientNetb0), which were discovered through extensive experimentation and employed to extract features, outperform other CNN combinations and ensembles. The features extracted by each DCNN are slightly different intuitively. Multi-CNN feature extraction produces a superset of features that outperforms single-CNN feature extraction. One-versus-one SVM effectively performs 8-class Bc classification: was used as a heuristic technique such as a one-versus-one binary classification model for multi-class classification. With eight target classes for the breast cancer BreakHis dataset, a total of 28 SVM classifiers are modeled based on the \(\left(k(k-1)\right)/2\) formula, where \(k\) is the number of classes.