## 5 Experimental Results
### Performance Analysis  
In this study, a binary classification approach was used to classify benign and malignant samples of prostate tissue. Two levels of classification were performed: DL (based on images) and ML (based on features). Table 4 shows the comparative analysis between the optimizers for model 1 and model 2, respectively. The developed LWCNN models were trained a couple of times by changing the optimizers during training.  
From the above comparison table, we can analyze that the Adadelta performed the best and gave the best accuracies on test data for both the architectures. SGD and Adam performed close to Adadelta for model 2. On the other hand, RMSProp performed close to Adadelta for model 1. However, Adadelta (update version of Adam and Adagrad) is a more robust optimizer that restricts the window of accumulated past gradients to some fixed size \(w\) instead of accumulating all past square gradients. The comparison of these optimizers revealed that Aadelta is more stable and more rapid, hence, an overall improvement on SGD, RMSProp, and Adam. The behavior and performance of the optimizers were analyzed using the receiver operating characteristic (ROC) curve. It is a probabilistic curve that represents the diagnostic ability of a binary classifier system, including an indication of its effective threshold value. The area under the ROC curve (AUC) summarizes the extent to which a model can separate the two classes. Figure 9a,b show the ROC curve and corresponding AUC that depicts the effectiveness of different optimizers used for model 1 and model 2, respectively. For model 1, the AUCs were 0.95, 0.94, 0.96, and 0.93, and for model 2, 0.98, 0.97, 0.98, and 0.97 were obtained using Adadelta, RMSProp, SGD, and Adam, respectively.  
Further, based on the optimum accuracy in Table 4, we carried out EML classification using the CNN extracted features from model 2, to analyze the efficiency of ML algorithms. Also, handcrafted features classification was performed to compare the performance with the non-handcrafted features classification results. Moreover, the EML model achieved promising results using the CNN-based features. Model 2 outperformed model 1 in overall accuracy, precision, recall, F1-score, and MCC, with values of 94.0%, 94.2%, 92.9%, 93.5%, and 87.0%, respectively. A confusion matrix (Figure 10) was generated based on the LWCNN model that yielded the optimum results, and thus most reliably distinguished malignant from benign tissue. Benign tissue was labeled as \"0\" and malignant was labeled as \"1\" to plot the confusion matrix for this binary classification. The four squares in the confusion matrix represent true positive, true negative, false positive, and false negative; their values were calculated using the test dataset based on the expected outcome and number of predictions of each class. Tables 5 and 6 show the overall comparative analysis for the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model-1**} & \multicolumn{2}{c}{**Model-2**} \\ \hline \multirow{2}{*}{**Optimizers**} & **Test Loss** & **Test Accuracy** & **Test Loss** & **Test Accuracy** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline SGD & 0.51 & 85.7 & 0.25 & 93.3 \\ RMSProp & 1.00 & 85.5 & 0.62 & 89.3 \\ Adam & 0.45 & 84.4 & 0.28 & 91.1 \\ Adadelta & 0.54 & 89.1 & 0.25 & 94.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the optimizers for tissue image classification.