# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 5 Experimental Studies
### Experimental Results  
This section evaluates and compares the performance of seven distinct CNN architectures, namely Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19, in accurately classifying CT kidney images into cyst, normal, stone, and tumor classes.  
The architectures underwent training using same training dataset and hyperparameters, and their performance was evaluated using comparable test datasets. Strict consistency was maintained throughout the training procedure to ensure fair comparison of the model performance in both training and testing stages. DP-Adam was utilized as the optimizer for privacy-aware classification, whereas Adam was used for traditional classification tasks. The parameters assigned to the learning rate, batch size, number of epochs, number of microbatch, l2 norm clip, and number of epochs are 0.0001, 40, 100, 40, 1, and 100, respectively. The loss function utilized was categorical cross-entropy, whereas the activation functions applied were ReLU and Softmax. Application of the trained model enabled the generation of predictions on the test dataset, which were then compared with the true labels. Consequently, the classification performance of each model in distinguishing kidney cysts, normal tissues, stones, and tumors was assessed.  
The performance results of Xception, ResNet50, InceptionResnetV2, MobileNet, VGG19, InceptionV3, and DenseNet201 architectures were evaluated and described, both with and without privacy modifications. Presented in Table 2 are the traditional classification results, whereas Tables 3-5 shows the test results of the proposed framework with different noise_multiplier (nm) and \(\delta\) results. Generally, the value of \(\delta\) is determined as 1 \(\times\) 10\({}^{-5}\) in the literature. Therefore, throughout the experiments, we set \(\delta\) to 1 \(\times\) 10\({}^{-5}\). The images presented in Figures 11-16 were created according to the best results obtained, as in Table 3. Figures 11-13 provide the confusion matrix for each class, the accuracy rates of the training and test data, and the ROC curves for traditional classification. Given that Table 3 yielded superior outcomes in the studies, Figures 14-16 were generated based on the values indicated in Table 3. Presented in Figures 14-16 are the confusion matrices for each class, the accuracy rates of the training and test data, and the ROC curves for privacy-aware classification via the proposed framework. Among the architectures included in Table 1, VGG19 outperformed other architectures in traditional classification tasks. Furthermore, alternative algorithms also yielded robust outcomes for classification in terms of accuracy, recall, F-1, and precision.  
Figure 11: Confusion matrix of traditional classification.  
Figure 12: Accuracy graph of traditional classification.  
Figure 13: ROC curves of traditional classification.  
Figure 14: Confusion matrix of privacy-preserved classification.  
Figure 15: Accuracy graph of privacy-preserved classification.  
Figure 16: ROC curves of privacy-preserved classification.