# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 2 Literature Review  
The main purpose of this section is to review the studies on detecting diseases using a CNN-based architecture with CT images. Additionally, it provides a table for future research by comparing accuracy ratios and datasets acquired from various catalogs utilized in the current literature. Below is a summary of research in the literature that investigates the use of these architectures with CT scans for different disease detection. In [10], the authors assessed the efficiency of deep learning techniques in the task of liver segmentation. Comparative analyses were conducted on two U-Net architectures of varying sizes and a basic CNN model. The U-Net model triggered by the ReLU exhibited a highly successful performance with an F1 score of 97.58% when trained on a 256 \(\times\) 256 input size. In comparison, the U-Net model trained on a 512 \(\times\) 512 input size showed a 2.61% reduction in the F1 score. The F1 score values of the U-Net models trained with sigmoid and linear activation functions showed a notable reduction in comparison to the 256 \(\times\) 256 U-Net model. In [11], [PERSON] introduced a 3D U-Net model that incorporates an early fusion technique for the purpose of automatically segmenting organs on CT scans. The proposed model employs a two-stage early fusion technique that incorporates CT scans with distinct color spaces. High accuracy rates were attained in the tests for large-size organs; however, the degree of success was limited for small-size organs. Training and testing of the proposed model yielded an average segmentation accuracy of 88.8% and 79.6% for all organs, respectively. The automated segmentation procedure produced successful and promising outcomes. However, further inclusion of training and test data could enhance the model's performance and enable advances in segmenting tiny-size organs. In comparison to CNN and FCN architectures, the proposed approach demonstrated superior performance. In [12], [PERSON] examined the advancement of a deep learning system for the purpose of diagnosing COVID -19 using chest CT images. The present system comprises two distinct models that possess both classification and semantic segmentation functionalities. This study employed the EfficientNetB2 model as the classification model and achieved a 99.75% accuracy in classifying chest CT scans as either positive or negative for COVID-19. The U-Net model was employed for semantic segmentation, and a ResNet50-based model achieved the highest performance, producing a Jaccard index of 93.76% and a Dice score of 96.61%. [PERSON] et al. [13] explored the application of CT scans in the diagnosis of COVID-19 and assessed the efficacy of machine learning and transfer learning models. A COVID-CT dataset, which included 349 positive and 397 negative cases, was employed. Extraction of shape, statistical, and texture information from CT images was followed by classification using different classifiers. Further classification was conducted using transfer learning models. The findings indicate that the XGBoost classifier achieved the highest accuracy of 98.65%, whereas VGG19 and the ResNet50 model achieved an accuracy of 98.04% and 99.62%, respectively. The utility of image processing technology in the diagnosis of lung cancer was examined in [14]. A dataset comprising CT images of lung, including healthy, malignant, and benign cases, was utilized. This study aimed to develop a lung cancer diagnosis system by leveraging pretrained convolutional neural networks and a novel hybrid model. Empiricalstudies have revealed that the developed model greatly enhances the precision of lung cancer diagnosis. Although the accuracy rate by deep learning architectures only reached 92%, the proposed approach demonstrated a notable result with an accuracy of 98.3%. The performance of CNNs for predicting the risk of COVID-19 is assessed in [15]. The study utilized CT scans acquired from clinically diagnosed COVID-19 patients.