## 7 Conclusions  
We have proposed a multi-scale pooled image feature representation (MPIFR) deep learning architecture with one-versus-one SVM for 8-class BC histopathological image classification. The proposed method of four pre-trained DCNN architectures (ResNet50, ResNet18, DenseNet201, and EfficientNetb0) to extract highly predictive multi-scale pooled image feature representation (MPIFR) from four resolutions (40X, 100X, 200X, and 400X) of BC images that are then classified using one-versus all SVM has been presented. In rigorous evaluation, the proposed MPIFR method achieved an average accuracy of 97.77%, with 97.48% sensitivity, and 98.45% precision on the BreakHis histopathological BC image \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & **AI-Based Screening in** & **AI-Based Screening in** & **AI-Based Grading** \\  & **Low Incidence of** & **High Incidence of** & **System** \\  & **Cancer** & **Cancer** & \\ \hline Accuracy measure & high & high & high \\ \hline Sensitivity measure & high & optional & high \\ \hline Specificity measure & high & optional & high \\ \hline Model complexity & small & very small & very small \\ \hline Model robustness & high & high & high \\ \hline Prospective study & needed & needed & needed \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Criteria for selecting machine learning techniques to support the decision of classifying BC from histopathological images. dataset, outperforming the prior state-of-the-art for histopathological breast cancer multi-class classification and a comprehensive set of DCNN baseline models. With an evaluation based on a prospective study, the proposed ensemble model can reliably aid diagnosis for BC histopathology at high precision and sensitivity scores. Model complexity measures indicate how fast the model can take in the histopathological images and produce a classification result. Further, model complexity specification can be in terms of storage space. A lightweight model can be embedded into mobile apps in developing countries. A robust model should be able to accommodate images at a vary magnification factors. An AI-based model with only a high accuracy measure and having passed a prospective study can be considered in high BC incidence regions as a rapid screening system. In a future study, we will investigate other possible pre-trained models, particularly convNet with fewer connections. This will give a lightweight grading system for use in places with low access to computational resources. [PERSON], [PERSON], [PERSON] and [PERSON] conceived the presented idea; [PERSON], [PERSON] and [PERSON] developed the theory and performed the computations; [PERSON], [PERSON] and [PERSON] verified the analytical methods; [PERSON], [PERSON] and [PERSON] encouraged [PERSON] to investigate the algorithm used for this work and supervised the findings of this work. [PERSON] and [PERSON] were involved in the conceptualization and supervision of the research. All authors discussed the results and contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript. This research was supported in part by World Bank through the African Centre of Excellence Program hosted by Pan African Materials Institute (AUST/PAMI/2015 5415-NG); the WPI Global Impact Fellowship Program, the WPI Innovation Fellowship, and the African Development Bank (AFDB). The research utilized a de-identified, public dataset and IRB approval was not necessary. This study is based on the publically available BreakHis dataset from the laboratory of Vision, Robotics, and Imaging (VRI), Federal University of Parana (FUPR).