## 6 Discussion
The result of the one-vs-one technique is presented in Figure 17.  
Our proposed MPIFR method outperforms the state-of-the-art for 8-class BC classification: as shown in Table 1 in which various proposed BC image classification models are compared in terms of the proposed method, classification type, accuracy, F1-score, specificity, and AUC. All models in the table utilize the BreakHis breast cancer image datasets. As seen in Table 1, the proposed MPIFR model built for 8-class classification performed better than the other state-of-the-art multi-class models. Multiclassification of breast cancer images into eight (8) subtypes of malignant and benign are much more challenging [39]. It can be the basis of a computer-aided grading diagnosis system for BC histopathology. Compared to [PERSON] et al. [28], [PERSON] et al. [30] etc., the proposed ensemble model outperformed (97.77%) the other state-of-the-art multi-class BC classifiers. Besides that, our study and that of [PERSON] et al. are the only ones to perform eight (8) class classifications. The other studies performed four (4) class classifications. [PERSON] et al., in their study, used the structured deep learning model, and did not perform feature extraction using pre-trained DCNNs, achieving an accuracy of 93.2% on histopathological images. However, the proposed ensemble model in this study extract features from four pre-trained models and trains SVM classifiers in a one-vs-one approach. The trained model classifies test subset BreakHis images irrespective of the difference in magnification factor. Hypothetically this means that the proposed ensemble model will classify the image with high accuracy regardless of the difference in magnification of the input image. In addition, despite ensemble architecture consisting of multiple baseline models and multiple classifiers, the built model is a single model with reduced generalization error of the prediction. Except for [PERSON] et al. [28], the existing models reported only performance accuracy. Other relevant metrics such as precision, sensitivity, and specificity measures are missing. They are crucial to deciding the consistency and significance of an AI-based BC medical screening and grading system. We present criteria for selecting machine learning techniques to support the decision of classifying BC from histopathological images in Table 10. One of the objectives of using an AI-based application is to assist medical specialists with rapid screening for disease by assessing medical images and deciding on the presence or absence of a specific medical condition. A more complex AI-based application can further assess medical images and diagnose the extent or grade of a specified medical condition. The former is an AI-based screening system, and the latter is AI-based grading system. Deep learning is a state-of-the-art machine learning (ML) technique, which has been highly successful in various computer vision and image analysis tasks, substantially outperforming all clinical image analysis techniques [25]. Although deep learning models outperform other traditional clinical image analysis techniques, they are still susceptible to false positive and false negative rates. For these reasons, some criteria should be considered when selecting a deep learning model as an AI-based medical system. Table 11 presents criteria for selecting machine learning techniques to support the decision of BC classification from histopathological images. A deep learning algorithm trained to model BC classification should only be adopted for an AI-based screening or grading system after it has passed a prospective study test. The prospective study is carried out when both a licensed specialist and an AI-based system independently examine BC histopathological images from the same person/patient. The prospective study will compare the diagnostic capability of an AI-based model with respect to actual oncologists evaluating the histopathological image in real-time.