## 7 Conclusions  
In this study, 2D image classification was performed using PCa samples by leveraging non-handcrafted and handcrafted texture features to distinguish a malignant state of tissue from a benign state. We have presented LWCNN- and EML-based image and feature classification using feature map analysis. The DL models were designed with only a few CNN layers and trained with a small number of parameters. The computed feature maps of each layer were fed into these fully CNNs through the flattening and GAP layers, enabling binary classification using sigmoid and softmax classifiers. GAP and softmax were used for model 2, the optimal network in this paper. The GAP layer was used, instead of flattening, to minimize overfitting by reducing the total number of parameters in the model. This layer computes the mean value for each feature map, whereas flattening combined all feature maps extracted from the final convolution or pooling layers by changing the shape of the data from a 2D matrix of features into a one-dimensional array for passage to the fully CNN classifier. A comparative analysis was performed between the DL and EML classification results. Moreover, the computational cost was also compared among the models. The optimum LWCNN (i.e., model 2) and EML models (a combination of LR and RF classifiers) achieved nearly perfectly accurate results with significantly fewer trainable parameters. The proposed LWCNN model developed in the study achieved an overall accuracy of 94%, average precision of 94.2%, an average recall of 92.9%, an average f1-score of 93.5%, and MCC of 87%. On the other hand, using CNN-based features, the EML model achieved an overall accuracy of 92%, an average precision of 92.7%, an average recall of 91%, an average f1-score of 91.8%, and MCC of 83.5%.  
To conclude, the analysis presented in this study is very encouraging. However, a model built for medical images may not work well for other types of images. There is a need to fine-tune the hyperparameters to control model overfitting and loss, thereby improving accuracy. The 2D LWCNN (model 2) developed in this study performed well, and therefore, the predicted true positive and true negative samples for benign and malignant, respectively, were plotted using Q-Q plots. The CAM technique was used to visualize the results of the block box CNN model. In the future, we will consider other methods and develop a more complex DL model and compare it with our optimal LWCNN model and other transfer learning models. Further, we will extend the research to multi-class classification (beyond binary) to simultaneously classify benign tissues, as well as grades 3-5.  
Funding acquisition, H.-K.C.; Methodology, S.B.; Resources, N.-H.C.; Supervision, H.-K.C.; Validation, H.-G.P.; Visualization, C.-H.K.; Writing--original draft, S.B.; Writing--review and editing, C.-H.K. and D.P. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the \"Regional Specialized Industry Development Program (R&D, P0002072)\" supervised by the Korea Institute for Advancement of Technology (KIAT). All subjects' written informed consent waived for their participation in the study, which was approved by the Institutional Ethics Committee at College of Medicine, Yonsei University, Korea (IRB no. 1-2018-0044). The authors declare that they have no conflicts of interest.