# Optimal Histopathological Classification Factors for Deep Learning-Based Breast Cancer Prediction
## 6 Discussion  
The histopathological uses different magnification factors to capture the unique manifestation of cell deformation. This is because every magnification factor reveals distinctive features of the tissue under investigation. For instance, high-power magnification factors are beneficial for examining tissues in great detail. On the other hand, using low-power magnification helps understand the tissues' overall structure. The disadvantage is that it can be difficult to distinguish between normal and abnormal tissues. Histopathologicals usually use different magnification factors to gain information from each level to construct a conclusion. The justification mentioned above holds for human histopathologicals, but does it also hold for machine learning algorithms? In particular, do CNNs benefit from training at various magnification levels? The work in this paper studies the impact of the magnification factor on the learning rate. The paper tries to answer: what is the best magnification factor so that the model achieves high prediction accuracy? Table 4 lists the work done in the literature about breast cancer classification using histopathological images. Except for the work in [6; 17], the literature did not discuss the effects of various magnification factors on the model's prediction accuracy. The authors in [17] show the accuracy difference among different magnification factors without describing their findings and providing a conclusion. The impact of various magnification factors on learning rate is not the focus of [17] compared to the work done in this paper. A study of the effects of various feature extraction, magnification factors, and classifiers is presented in [6]. The latter work aims not to study which magnification factors achieve a better learning rate; their focus is to evaluate the overall performance as there is no in-depth analysis or conclusion of which magnification factor is the best.  
This study thoroughly examines how different magnification factors affect the model's ability to predict classes, i.e., benign versus malignant. The results of this work showed that the model achieved a higher accuracy (learning rate) when using 200x and 400x magnification factors. The latter conclusion is noticeably clear when using the following performance matrices: precision, recall, F-Score, confusion matrix, and weighted average accuracy. The kernel density estimator shows that the model predicts higher probability values when using 200x and 400x magnification factors. This assures that the model finds satisfying features that make the model easy to learn and predict. The model has fewer predictions in the confusion area when learning from 200x and 400x magnification factors, as shown in Figure 15. The conclusion of this study is that it is advisable to use magnification factors of 200x to train and predict. It is worth trying to consider 200x as the center value of the magnification factors and using a magnification factor range (for example, n factor values less and higher than the center); the range is centered around 200x. This will be the future work of this paper.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Auth.** & **Dataset** & **Modality** & **Methodology** & **Preprocessing** & **Objective** \\ \hline
[3] & Kaggle invasive ductal carcinoma & HI & SVM and LR & Feature reduction using PCA and HOG and Canny edge detection & BC classification, i.e., B or M \\ \hline
[4] & BreakHis [21] & HI & SVM and LR & CNN (for feature extraction) + SVM (for classification) or CNN (for feature extraction) + LR (for classification) \\ \hline
[5] & Kaggle dataset & HI & SVM and CNN for feature extraction and K-means for tumor extraction. & Segmentation algorithm GA and K-means & locate and extract tumor cells \\ \hline \end{tabular}
\end{table}
Table 4: Literature review for BC prediction using ML trained on histopathological images.