## 4 Results
### Image Denoising Before Tumor Detection in CT Images  
This illustrates CT images showing the effectiveness of various denoising methods on tumor detection. From the results shown in Figure 6, the DnCNN has been shown to outperform AGF, U-Net, DWT, and DenseNet in preserving critical diagnostic details, as evidenced by higher SSIM (0.91) and PSNR (30.5) values and lower RMSE (0.048), indicating superior noise removal while retaining essential tumor features. A lackluster performance was noted in DWT, with the lowest performance metrics of SSIM of 0.81, PSNR of 26.9, and RMSE of 0.070. The yellow parts from numbers 1-5 in Figures 6 and 7 indicate tumorous regions. Figure 7 presents MRI images that illustrate tumor detection performance before denoising (BDn) and after denoising (ADn) using different methods. The DnCNN demonstrates better performance, achieving higher SSIM and PSNR, along with lower RMSE, compared to other methods. It was closely followed by DenseNet, even though it introduced blurring. Figures 8 and 9 illustrate the results of CT and MRI image denoising for tumor detection (BDn) and (ADn). The performance in both CT and MRI images indicates improved visual representation after denoising, enhancing the detection of tumors in low-quality images. The DnCNN outperformed other methods based on PSNR (29.5 dB) and SSIM (0.91) for CT images and a PSNR of 28.3 dB and SSIM of 0.89 for MRI images, as shown in Figures 8 and 9 and confirmed in Tables 2 and 3. The good performance of DnCNN is attributed to its ability to learn from a large dataset of noisy and clean image pairs, enabling it to better preserve important image features while removing noise. It was closely followed by DenseNet and contrasted with the poor output of AGF. Generally,  
the impact of CT images in Figure 8 was higher than the MRI images in Figure 9. This is attributed to MRI scans having more complex noise characteristics, such as spatially varying noise, which makes it harder for the model to generalize. Tables 2 and 3 below confirm the results of Figures 8 and 9, with DnCNN outperforming the rest in terms of PSNR, SSIM, and RMSE. The brain CT dataset and brain MRI dataset were used to test the capability of the proposed approach, and results are presented in Tables S1 and S2 and Figure S1. Figure 6: CT image performance in tumor detection before and after denoising using DnCNN, AGF, U-Net, DWT, and DenseNet. The yellow parts from numbers 1–5 indicate tumorous regions. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**No.** & **Method** & **SSIM (BDn)** & **SSIM (ADn)** & **PSNR (BDn)** & **PSNR (ADn)** & **RMSE (BDn)** & **RMSE (ADn)** \\ \hline
1 & DnCNN & 0.64 & 0.91 & 23.0 & 29.5 & 0.108 & 0.048 \\ \hline
2 & AGF & 0.61 & 0.84 & 22.3 & 28.4 & 0.114 & 0.052 \\ \hline
3 & U-Net & 0.60 & 0.83 & 22.0 & 27.6 & 0.120 & 0.065 \\ \hline
4 & DWT & 0.57 & 0.81 & 21.8 & 26.9 & 0.126 & 0.070 \\ \hline
5 & DenseNet & 0.62 & 0.86 & 22.7 & 29.1 & 0.112 & 0.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CT performance for tumor detection before denoising (BDn) and after denoising (ADn). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**No.** & **Method** & **SSIM (BDn)** & **SSIM (ADn)** & **PSNR (BDn)** & **PSNR (ADn)** & **RMSE (BDn)** & **RMSE (ADn)** \\ \hline
1 & DnCNN & 0.64 & 0.91 & 23.0 & 29.5 & 0.108 & 0.048 \\ \hline
2 & AGF & 0.61 & 0.84 & 22.3 & 28.4 & 0.114 & 0.052 \\ \hline
3 & U-Net & 0.60 & 0.83 & 22.0 & 27.6 & 0.120 & 0.065 \\ \hline
4 & DWT & 0.57 & 0.81 & 21.8 & 26.9 & 0.126 & 0.070 \\ \hline
5 & DenseNet & 0.62 & 0.86 & 22.7 & 29.1 & 0.112 & 0.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CT performance for tumor detection before denoising (BDn) and after denoising (ADn). Figure 7: MRI image performance in tumor detection before and after denoising using DnCNN, GF, U-Net, DWT, and DenseNet. The yellow parts from numbers 1–5 indicate tumorous regions. Figure 8: CT image performance in tumor detection BDn and ADn using DnCNN, AGF, U-Net, DWT, and DenseNet.