## 4 Results and Discussion

Table 4: Confusion matrix of SVM binary classification—Grade 4 vs. Grade 5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training: 91.7\%**} & \multicolumn{3}{c}{**Testing: 85.0\%**} \\ \hline
**Train** & **Grade 3** & **Grade 4+5** & **Data** & **Test** & **Grade 3** & **Grade 4+5** & **Data** \\ \hline Grade 3 & 55 & 5 & 60 & Grade 3 & 36 & 4 & 40 \\ \hline Grade 4+5 & 5 & 55 & 60 & Grade 4+5 & 8 & 32 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix of SVM binary classification—Grade 3 vs. Grade 4, 5. For the purpose of validation, we also performed prostate cancer grading classification using multilayer perceptron (MLP) technique in Weka, shown in Table 6. MLP is a class of feed-forward artificial neural network, which consists of at least three layers of node: an input layer, hidden layer, and an output layer. Each node is a neuron except input nodes and uses a non-linear activation function. MLP utilizes a supervised learning technique like SVM. From the results shown in Tables 5 and 6, we can see that the proposed SVM binary classification works significantly better than MLP, and the highest accuracy obtained was 92.5%, for Grade 4 vs. Grade 5. First, classification was performed to detect cancer in all of the samples in the dataset. The second and third classification was performed within the cancer group for low- and high-grade cancer detection. In Figure 7, the bar graph shows the comparison results for the three different binary divisions that are used for SVM classification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Training Accuracy (\%)** & **Testing Accuracy (\%)** \\ \hline Malignant vs. Benign & 99.0 & 81.0 \\ \hline Grade 3 vs. Grade 4, 5 & 98.0 & 75\% \\ \hline Grade 4 vs. Grade 5 & 97.5 & 76.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results for three binary divisions using the multilayer perceptron (MLP) classification technique. Figure 7: Comparison graph of support vector machine (SVM) classification accuracy among three binary divisions. The classification accuracies of the three groups are very close to each other, and the highest accuracy obtained was 92.50%, for grade 4 vs. grade 5. [PERSON]’s correlation coefficient (MCC) indicates the quality of binary classification among the three classification groups. To predict, automatically, prostate cancer gradings, we used machine learning and deep learning algorithms such as SVM and MLP, respectively. To do so, we first applied image segmentation as a preprocessing step. Secondly, we converted the images from RGB to binary to carry out watershed segmentation. Thirdly, we calculated a set of morphological features based on the segmented nucleus and lumen tissue images. Finally, the SVM and MLP classification was performed based on the significant features selected. We can see that the results of the comparison between SVM classification accuracy in Table 7 and Figure 8 vary between one-shot and binary classifiers. When we classified our data using multi-class or one-shot classifiers, the classification accuracies for benign, Grade 3, Grade 4, and Grade 5 are 60%, 55%, 85%, and 50%, respectively. Using the proposed binary classification approach, the accuracies for the same groups are 92.5%, 90.0%, 90.0%, and 95.0%, respectively. Comparing both classifiers simultaneously, we can see that the results obtained using the binary classifier are better than those obtained using multi-class or one-shot classifier. Table 8 shows the comparison results of MLP classifier between one-shot and binary classification. After comparing the results between SVM and MLP classification methods, we can say that the proposed method, SVM, achieved better results than MLP. In one-shot classification, the entire dataset is classified into four groups simultaneously. In this case, the errors in one class affect the performance of the others, negatively impacting the classification accuracy. Thus, the model cannot make correct predictions.