## 6 Discussion
The modified, model 2 was adequate for the classification of benign and malignant tissue images. Our study examined the capability of the proposed LWCNN model to detect and forecast the histopathology images; a single activation map was extracted from each block (see Figure 13) to visualize the detection results using a heat map. Notably, we used an EML method for non-handcrafted and handcrafted features classification. However, the EML model was sufficiently powerful to classify the computational features extracted using the optimal LWCNN model, which predicted the samples of benign and malignant tissues almost perfectly accurately. Also, tissue samples that were classified and predicted using the softmax classifier are shown in quantile-quantile (Q-Q) plots of the prediction probability confidence for benign and malignant states in Figure 14a,b, respectively. These Q-Q plots allowed for the analysis of predictions. True and predicted probabilistic values were plotted according to true positive and true negative classifications of samples (see Figure 9), respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Parameter** & **Model** & **Time to Solution (Minutes)** \\ \cline{2-5}  & **(Trainable)** & **Memory Usage** & **Train** & **Test** \\ \hline VGG-16 & 27,823,938 & 60.9 MB & 570 & \textless{}1 \\ ResNet-50 & 23,538,690 & 148.4 MB & 660 & \textless{}1 \\ Inception-V3 & 22,852,898 & 66.9 MB & 600 & \textless{}1 \\ DenseNet-121 & 7,479,682 & 199.7 MB & 700 & \textless{}1 \\ Model-2 (LWCNN) & 5,386,638 & 44.7 MB & 190 & \textless{}1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing performance and computation cost of model-2 with other pre-trained models. In Q-Q plots, note that the black bar at the top parallel to the x-axis shows true probabilistic values; red (true positive) and blue (true negative) markers show the prediction confidence of each sample of a specific class. We used a softmax classifier, which normalizes the output of each unit to be between 0 and 1, ensuring that the probabilities always sum to 1. The number of samples used for each class was 600; the numbers correctly classified were 565 and 557 for true positive and true negative, respectively. A predicted probability value \(>\) 0.5 and \(<\)0.5 signifies an accurate classification and misclassification, respectively. Figure 14: Quantile-quantile plot for true and predicted probabilistic values. (**a**) Samples that were benign and had true positive predictions. (**b**) Samples that were malignant and had true negative predictions. Figure 13: Visualizations of class activation maps generated from model 2, created using different numbers of filters. Outputs of (**a**) first convolutional, (**b**) second convolutional, (**c**) third convolutional, and (**d**) classification blocks. Colors indicate the most relevant regions for predicting the class of these histopathology images, as detected by the convolutional neural network. The combination of image-feature engineering and ML classification has shown remarkable performance in terms of medical image analysis and classification. In contrast, CNN adaptively learns various image features to perform image transformation, focusing on features that are highly predictive for a specific learning objective [65]. For instance, images of benign and malignant tissues could be presented to a network composed of convolutional layers with different numbers of filters that detect computational features and highlight the pixel pattern in each image. Based on these patterns, the network could use sigmoid and softmax classifiers to learn the extracted and important features, respectively. In DL, the \"pipeline\" of CNN's processing (i.e., from inputs to any output prediction) is opaque [66], performed automatically like a passage through a \"black box\" tunnel, where the user remains fully unaware of the process details. It is difficult to examine a CNN layer-by-layer.