# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 3 Material and Methods
### Cnn  
CNN is extensively and efficiently utilized among deep learning models. The concept of CNN was initially proposed by [PERSON] in the 1980s. [PERSON] and his team subsequently integrated CNN with backpropagation theory to accurately identify handwritten numbers and documents. It is highly advantageous for the processing and analysis of visual data, particularly images and videos. The primary advantage of this technology lies in its capacity to acquire hierarchical representations, therefore facilitating a remarkable transformation in the domains of image processing and computer vision through direct learning from pixel data [20]. Medical image processing has seen the extensive application of CNN in recent years. This model demonstrates exceptional performance in tasks including object identification and recognition, image classification, and segmentation [21, 22]. The CNN's capacity to extract significant characteristics and its high performance have established it as a favored option, particularly for the processing of complicated medical images, like CT scans [15]. A CNN is composed of several layers that convert inputs into smaller regions using convolution filters [3]. Multi-layer architecture is employed to identify visual characteristics. These layers are partitioned into two distinct stages: extraction of features and classification. The feature extraction phase consists of an input layer, a convolution layer, and a pooling layer, whereas the classification phase consists of a fully connected layer and an output module [23]. The input data are provided to the network in their raw form, and the resulting resolution directly impacts the network's performance. Applying filters, the convolution layer derives feature maps from the incoming data. Efficient extraction of visual features requires the use of a specific number of filters. Filters are shifted over the image and matrix multiplications are executed. Summation of the values derived from the multiplications \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & \begin{tabular}{c} **Main** \\ **Architecture** \\ \end{tabular} & **Data Type** & **Focus** & \begin{tabular}{c} **Consider** \\ **Privacy** \\ \end{tabular} & **Dataset** & \
\begin{tabular}{c} **Number** \\ **of Records** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline
[10] & CNN & CT & Liver & No &
\begin{tabular}{c} Combined Healthy Abdominal Organ \\ Segmentation \\ \end{tabular} & 2579 & 97.58 \\
[11] & CNN & CT & Multiple & No & MICCAI 2015 Multi-Atlas Abdomen & 3000 & 88.8 \\
[12] & CNN & CT & Organs & No &
\begin{tabular}{c} Labeling Challenge \\ EPSCH-19 \\ 19.685 \\ \end{tabular} & 4000 & 99.75 \\
[14] & CNN & CT & Chest & No & CT Scans for COVID-19 Classification & 19.685 & 99.62 \\
[15] & CNN & CT & Lung & No &
\begin{tabular}{c} IQ-MT/NCCD \\ COVID-CT Dataset \\ \end{tabular} & 1190 & 92.69 \\
[16] & CNN & CT & Chest & No & COVID-CT Dataset & 746 & 81 \\
[16] & CNN & CT & Kidney & No & PACS Systems in Dhaka, Bangladesh & 12.510 & 99.36 \\
[17] & CNN & CT & Lung & No & Data Science Bowl and Kaggle Lung CT & 285,058 & 96.36 \\
[18] & CNN & CT & Liver & No & 3 Dircadb Dataset & 2074 & 95.4 \\
[6] & CNN & CT & Kidney & No & PACS Systems in Dhaka, Bangladesh & 12.510 & 98.66 \\
[19] & CNN & CT & Lung & No & Original & 263 & 68.12 \\ This Study & CNN & CT & Kidney & Yes & PACS Systems in Dhaka, Bangladesh & 12,400 & 99.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of previous studies. yields the final value. This layer serves the function of identifying patterns in areas of the image that are often invisible to the human visual system. The initial convolution layers extract features at a lower level, whereas the deepest convolution layers extract features at a higher level. The majority of the intensive processing required by the CNN is carried out in this layer [24]. By decreasing the number and size of the network's parameters, the pooling layer decreases the computational cost and minimizes memory usage.