Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection

Abstract

This paper introduces a new privacy-preserving transfer learning framework for the classification of kidney diseases. In the proposed framework, transfer learning is employed for feature extraction, and differential privacy is used to obtain noisy gradients. A variety of CNN architectures, including Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19 are utilized to evaluate the proposed framework. Analysis of a large dataset of 12,400 labeled kidney CT images shows that transfer learning architectures based on the proposed framework achieve excellent accuracy ratios in privacy-preserving classification. These results demonstrate the effectiveness of the proposed framework in enabling transfer learning models to classify kidney diseases while ensuring privacy. The MobileNet architecture stands out for its exceptional performance, with an impressive accuracy of 99.83% in privacy-preserving classification. Considering the findings of this study, it is evident that the proposed framework is appropriate for the early and private diagnosis of kidney diseases and promotes the achievement of promising results in this field.

Keywords

• transfer learning
• differential privacy
• kidney disease detection