## 5 Conclusions  
In this study, we have developed a computerized grading system for digitized histopathology images using supervised learning methods. The segmentation process for biopsy tissue image was performed using the k-means algorithm and touching cells were separated using the watershed algorithm. Morphological features were selected for prostate cancer grading and diagnosis. Gaussian and linear kernels were used for the classification of prostate histopathological images. Using these kernels, we observed some improvements in the results, and gradually increased the performance of the model used for training and testing. The parameters of the kernel play a vital role in the classification process, and the best combination of \(C\) and \(\gamma\) was selected for better classification accuracy. Satisfactory classification results were obtained using the extracted morphological features, and these features were extracted from the sub-images, viewable in 40\(\times\) magnification. The quantitative analysis described here is remarkably flexible in terms of implementation. The SVM binary classification method presented in this paper is used to classify malignant vs. benign, Grade 3 vs. Grade 4+5, and Grade 4 vs. Grade 5. Our results are satisfactory and comparable with those reported in the literature and produced quantitative measures based on the features extracted from microscopic biopsy tissue images. In order to justify our proposed method, SVM, we also carried out features classification using MLP. One-shot and binary classification results were compared to show the differences in two classifications accuracies. In future studies, we will improve our classification accuracy using the combinations \begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Classification Methods** & **Classes** & **Accuracy** \\ \hline [PERSON] et al. (2007) [4] & \(k\)NN & Malignant vs. Benign & 96.7\% \\ \cline{3-4}  & & Low vs. High Grade & 81.0\% \\ \hline \multirow{3}{*}{[PERSON] et al. (2012) [5]} & \multirow{3}{*}{Decision Tree (DT)} & Grade 3 & 77.0\% \\ \cline{3-4}  & & Grade 4 & 76.0\% \\ \cline{3-4}  & & Grade 5 & 95.0\% \\ \hline \multirow{3}{*}{[PERSON] et al. (2018) [6]} & \multirow{3}{*}{SVM} & Malignant vs. Benign & 88.5\% \\ \cline{3-4}  & & Low vs. High Grade & 73.8\% \\ \hline [PERSON] et al. (2006) [7] & Bayesian & Malignant vs. Benign & 88.0\% \\ \hline [PERSON] et al. [8] & Fuzzy C-Means & Multispectral (Tw1 \& Tw2) & 90.77\% \\ \hline \multirow{3}{*}{[PERSON] et al. [11]} & \multirow{3}{*}{SVM} & Grade 3 vs. Grade 4 & 95.19\% \\ \cline{3-4}  & & Benign vs. Grade 3 & 86.35\% \\ \cline{3-4}  & & Benign vs. Grade 4 & 92.90\% \\ \hline \multirow{3}{*}{[PERSON] et al. (2017) [12]} & \multirow{3}{*}{SVM} & Grade 3 vs. Grade 4 & 88.9\% \\ \cline{3-4}  & & Benign vs. Grade 3 & 97.9\% \\ \cline{3-4}  & & Benign vs. Grade 4 & 92.4\% \\ \hline \multirow{3}{*}{[PERSON] et al. (2012) [13]} & \multirow{3}{*}{SVM} & Benign, Grade 3, and Grade 4 carcinoma & 85.6\% \\ \cline{3-4}  & & Malignant vs. Benign & 88.7\% \\ \cline{3-4}  & & Low vs. High Grade & 85.0\% \\ \cline{3-4}  & & Grade 4 vs. Grade 5 & 92.5\% \\ \cline{3-4}  & & Grade 3 & 90.0\% \\ \cline{3-4}  & & Grade 4 & 90.0\% \\ \cline{3-4}  & & Grade 5 & 95.0\% \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison between the proposed method and other standard methods for the classification of prostate cancer gradings. of multiple features. Deep learning and machine learning techniques will be used for comparative analysis, where, image classification will be performed using the convolutional neural network (CNN) and feature classification will be performed using support vector machine (SVM), respectively. Conceptualization, S.B., H.-G.P. and N.M.; Formal analysis, S.B., D.P., J.-H.S. and N.-H.C.; Methodology, S.B. ; Project administration, C.-H.K. ; Resources, H.-G.P., C.-H.K. and N.-H.C.; Supervision, H.-K.C. ; Validation, S.B. ; Visualization, N.M., J.-H.S., N.-H.C. and H.-K.C. ; Writing--original draft, S.B. ; Writing--review & editing, N.M.