# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 2 Literature Review
The collection comprises 349 CT pictures that are positive and 397 CT images that are negative, extracted from 216 people. Analysis and evaluation were conducted on CNN-based architectures VGG16, ResNet18, ResNet50, DenseNet121, DenseNet169, EfficientNetB0, and EfficientNetB1. The use of pretrained weights resulted in a performance enhancement of approximately 10-15% for all networks, with EfficientNetB1 and DenseNet169 achieving an accuracy level above 85%. In their study [16], [PERSON] et al. proposed a CNN model for accurate diagnosis of kidney diseases. The researchers utilized a total of 12,510 CT images obtained from distinct hospitals. The images were classified into four distinct categories all pertaining to kidney health. A CNN-based classification model was developed. Sigmoid and ReLU activation functions were employed. Analysis of the model's performance was conducted using visual representations, such as a confusion matrix and performance table. The statistical analysis revealed that 99.3% of the whole samples in the test set were accurately classified. The study by [PERSON] [17] introduces deep learning techniques for diagnosing lung cancer using lung CT images. The study utilized lung CT images obtained from the Data Science Bowl and Kaggle databases. The classification task utilized the AlexNet and GoogleNet designs, resulting in accuracy rates of 95.919% and 96.360%, respectively. [PERSON] et al. [18] introduced a novel approach to facilitate the identification of liver disease. Here, they provide an enhanced Deeplabv3+ model specifically designed for segmenting the liver in CT scans. The main enhancement of Deeplabv3+ is the substitution of its backbone with ResNet50, which aims to prevent the occurrence of vanishing gradients. Moreover, a channel attention module is incorporated into every ResNet50 residual block, enabling the network to selectively concentrate on significant information. The incorporation of transfer learning in the improved network serves to expedite the merging process and mitigate the potential for overfitting when dealing with a restricted dataset. By attaining an average union over intersection of 95.40% and a Dice coefficient of 94.80%, the proposed technique surpasses U-net and U-net++, demonstrating exceptional accuracy in liver segmentation. The study of [PERSON] et al. [6] demonstrates the efficiency of neural networks in the early detection of chronic kidney disease. Classification of kidney CT images was performed using three distinct neural network classification approaches. Initially, the images underwent watershed segmentation procedures. Next, many neural networks, including EAnet, ResNet50, and a proposed CNN model, underwent training using a dataset that is accessible to the public on Kaggle. The test findings indicate that EANet, ResNet50, and the proposed CNN model attained accuracy rates of 83.65%, 87.92%, and 98.66%, respectively on the test set of classification models. The proposed CNN model demonstrates superior sensitivity and specificity, resulting in the highest overall accuracy. A study conducted by [PERSON] et al. [19] examined deep learning techniques for the detection of lesions in CT scans of COVID-19. This approach, designed to alleviate the workload of physicians in the diagnostic procedure, is founded on the Faster R-CNN architectural framework. In order to achieve more precise outcomes, the data underwent initial clustering using Kmeans++. Subsequently, the Faster R-CNN model was evaluated using two distinct backbone networks, namely VGG16 and ResNet50. Experimental evaluations were conducted on both the raw dataset and the enhanced dataset. The Faster R-CNN model with the VGG16 backbone demonstrated superior performance on the enriched dataset. Using all test data, this model attained a recall of 68.12% and a precision of 65.58%. The percentage of misclassification was observed as 31.88%. Consequently, the aforementioned studies have shown that deep learning models yield highly effective outcomes in several classification tasks on medical images.