## 5 Experimental Results
### Visualization Results  
The CAM technique was used to visualize the results from an activation layer (softmax) of the classification block. CAM is used to deduce which regions of an image are used by a CNN to recognize the precise class or group it contains [22; 64]. Typically, it is difficult to visualize the results from hidden layers of a black box CNN model. More complexity is observed in feature maps with increasing depth in the network; thus, each image becomes increasingly abstract, encoding less information than the initial layers and appearing more blurred. Figure 11 shows the CAM results, indicating the method by which our DL network detected important regions; moreover, the network had learned a built-in mechanism to determine which regions merited attention. Therefore, this decision process was extremely useful in the classification network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Ensemble Machine Learning**} \\ \hline  & **CNN-Based** & **OCLBP** & **IOLBP** & **OCLBP + IOCLBP** \\ \hline Accuracy & 92.0\% & 69.3\% & 83.6\% & 85.0\% \\ Precision & 92.7\% & 66.0\% & 83.2\% & 85.5\% \\ Recall & 91.0\% & 70.6\% & 83.9\% & 84.5\% \\ F1-Score & 91.8\% & 68.2\% & 83.5\% & 85.0\% \\ MCC & 83.5\% & 38.6\% & 67.2\% & 69.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of non-handcrafted and handcrafted features classification. Metrics are for the test dataset.  
Figure 11: Class activation maps are extracted from one of the classification layers of our convolutional neural network. These show how images are classified and predicted by the neural network, although it is a black-box model. Top and bottom pairs of rows depict benign and malignant tissue images, respectively. (**a**) Input images with an RGB color scheme visualized as grayscale. (**b**) Activation map of classification block, showing detection of different regions in each tissue image. (**c**) Images overlaying (**a**,**b**), with spots indicating significant regions that the convolutional neural network used to identify a specific in that image.  
Our CNN detected specific regions using the softmax classifier by incorporating spatially averaged information extracted by the GAP layer from the last convolution layer, which had an output shape of \(14\times 14\times 384\). The detected regions depicted in Figure 11c were generated by the application of a heat map to the CAM image in Figure 11b and overlaying that on the original image from Figure 11a. A heat map is highly effective for tissue image analysis; in this instance, it showed how the CNN detected each region of the image that is important for cancer classification. Doctors can use this information to better understand the classification (i.e., how the neural network predicted the presence of cancer in an image, based on the relevant regions). The visualization process was carried out using the test dataset, which was fed into the trained network of model 2.  
In this study, supervised classification was performed for cancer grading, whereby our dataset was labeled with \"0\" and \"1\" to categorize benign and malignant tissue separately and independently. The probability distributions of data were similar in training and test sets, but the test dataset was independent of the training dataset. Therefore, after the model had been trained with several binary labeled cancer images, the unanalyzed dataset was fed to the network for accurate prediction between binary classes. Figure 12 shows examples of the binary classification results from our proposed model 2, with examples of images that were and were not predicted correctly. Notably, some images of benign were similar to malignant tissues and vice versa in terms of their nuclei distribution, intensity variation, and tissue texture. It was challenging for the model to correctly classify these images into the two groups.