# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 6 Conclusions and Discussion  
Technological advancements in medical imaging techniques have played a vital role in the early detection and management of illnesses. However, this process is vulnerable to human error since the task of visual interpretation is usually performed by professionals, who may not always be easily accessible. Hence, the use of automated technologies is always necessary to assist doctors in making diagnostic interpretations. However, in the literature, it is known that disease data are highly sensitive, and privacy measures should be taken to protect the privacy of patients against disclosure attacks. Disclosure of such kinds of data may negatively affect data owners, such as via discrimination, stigmatization, or financial harm. Hence privacy measures are always required. On the other hand, implementing privacy measures enables trust to be established between patients and healthcare providers. Thus, any useful process over these kinds of data, such as early diagnosis, treatment planning, etc., may be performed easily without considering if private data are disclosed. It is essential to use privacy-preserving measures for extracting important information from such data. The objective of this study was to provide a novel framework that considers the accurate classification of kidney diseases while ensuring data privacy. In the proposed framework, transfer learning was employed for feature extraction, and differential privacy was used to obtain noisy gradients. An evaluation of the proposed framework's efficiency was conducted by comparing the performance of seven distinct transfer learning archi \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Accuracy** & **Recall** & **F1** & **Precision** \\ \hline Xception & 0.9271 & 0.9271 & 0.9249 & 0.9265 \\ ResNet50 & 0.6672 & 0.6672 & 0.5829 & 0.6399 \\ InceptionResNetV2 & 0.9033 & 0.9035 & 0.9034 & 0.9033 \\ VGG19 & 0.8947 & 0.8947 & 0.8947 & 0.8944 \\ MobileNet & 0.9868 & 0.9868 & 0.9867 & 0.9868 \\ DenseNet201 & 0.9781 & 0.9781 & 0.9782 & 0.9781 \\ InceptionV3 & 0.8672 & 0.8672 & 0.8676 & 0.8676 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Privacy-aware classification results with the proposed framework (nm = 0.3, \(\varepsilon\) = 290, and \(\delta\) = \(1\times 10^{-5}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Accuracy** & **Recall** & **F1** & **Precision** \\ \hline Xception & 0.9736 & 0.9736 & 0.9734 & 0.9735 \\ ResNet50 & 0.7591 & 0.7591 & 0.7148 & 0.7347 \\ InceptionResNetV2 & 0.9537 & 0.9537 & 0.9534 & 0.9533 \\ VGG19 & 0.9400 & 0.9400 & 0.9381 & 0.9388 \\ MobileNet & 0.9983 & 0.9983 & 0.9983 & 0.9983 \\ DenseNet201 & 0.9875 & 0.9875 & 0.9875 & 0.9875 \\ InceptionV3 & 0.9392 & 0.9392 & 0.9383 & 0.9391 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Privacy-aware classification results with the proposed framework (nm = 0.1, \(\varepsilon\) = 772,577, and \(\delta\) = \(1\times 10^{-5}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Accuracy** & **Recall** & **F1** & **Precision** \\ \hline Xception & 0.9494 & 0.9494 & 0.9489 & 0.9489 \\ ResNet50 & 0.7051 & 0.7051 & 0.6431 & 0.6925 \\ InceptionResNetV2 & 0.9223 & 0.9223 & 0.9209 & 0.9210 \\ VGG19 & 0.9104 & 0.9102 & 0.9103 & 0.9102 \\ MobileNet & 0.9911 & 0.9911 & 0.9911 & 0.9911 \\ DenseNet201 & 0.9848 & 0.9848 & 0.9848 & 0.9848 \\ InceptionV3 & 0.8997 & 0.8997 & 0.8968 & 0.8998 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Privacy-aware classification results with the proposed framework (nm = 0.2, \(\varepsilon\) = 4625, and \(\delta\) = \(1\times 10^{-5}\)). tectures: Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19. Every architecture was assessed using accuracy, recall, F-1 score, and precision criteria. The acquired findings demonstrate the efficiency of each architecture in classification tasks.