## 3 Implementation Based on Generative Adversarial Network (GAN)
### Experimental Setup
Four classes were categorized into Figshare, SARTAJ, Br35H, and pituitary tumors. We selected 991 slice pairs from 8 patients for training and 320 slice pairs from 2 patients for testing. Additionally, we used a dataset from the Stanford Brain MRI dataset comprising 156 whole-brain MRI studies [33]. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Predictor** & **Original Form** & **Modification** & **Overall Performance** \\ \hline \hline \multirow{7}{*}{U-Net (Figure 4a)} & \multirow{7}{*}{-Unified one U-Net -No Padding applied} & \multirow{7}{*}{-Compared to U-Net in [11] our architecture is split into two parallel pathways (one for content and the other for noise).} & \multirow{7}{*}{-Enhances ability to separate and utilize noise-related and content features for effective capturing and processing.} \\  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{7}{*}{DnCNN (Figure 4b)} & \multirow{7}{*}{-DnCNN originally used residual learning, where the network learned to predict the noise rather than the proposed image itself} & \multirow{7}{*}{-In contrast with DrCNN in [13] this work discarded the residual learning since noise can dominate residuals.} & \multirow{7}{*}{-The network is trained directly to output the denoised image rather than the noise residual.} \\  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \multirow{7}{*}{DenseNet (Figure 4c)} & \multirow{7}{*}{-Used deconvolution layers} & \multirow{7}{*}{-Advrous Spatial Pyramid Pooling (ASPP) substituted bottleneck layers.} & \multirow{7}{*}{-Edsures between feature measurements and denoised images.} \\  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline CA-AGF (Figure 4d) (Figure 5) & \multirow{7}{*}{-Traditional AGF has no dynamism in the filtering process} & \multirow{7}{*}{-The Context-Aware AGF dynamically adjusts the filtering process based on local image structures.} & \multirow{7}{*}{-Enhances feature preservation and denoising adaptability in complex regions like medical images.} \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline DWT (Figure 4e) & \multirow{7}{*}{-Traditional DWT produces less natural-looking and less detailed denoised images.} & \multirow{7}{*}{-Enhanced with multi-resolution fusion and anisotropic diffusion to preserve fine details and broader structures.} & \multirow{7}{*}{-Produces more natural-looking and detailed denoised images compared to the original DWT.} \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of contributions of predictors (DrCNN, DenseNet, U-Net, CA-AGF, and DWT) to the overall performance. Figure 4: \(Cont.\)  
(iii) Data Preparation and transformation  
In this experiment, we focused on MRI and CT images as our datasets. To thoroughly assess the efficacy of the proposed denoising and tumor detection approach, we evaluated ACNCL-U-Net, ACNCL-DnCNN, ACNCL-DenseNet, ACNCL-CA-AGF, and ACNCL-DWT using four distinct medical imaging datasets. These datasets span two imaging modalities commonly used in clinical diagnostics: CT and MRI. The noisy CT was collected under low-dose conditions, while the noisy MRI images resulted from k-space undersampling. Figure 5 illustrates examples of two image slices, highlighting the significant differences in noise patterns across the various imaging modalities. The degraded medical images, the corresponding clean images, and the noisy images in two medical imaging modalities are shown. Figure 4: (**a–e**) The CNN architectures, CA-AGF, and DWT utilized as predictors in our GAN-based implementation are illustrated.