## 4 Results
### Ablation Studies
When compared with comparison methods like the BM3D, RED-CNN, WGAN-GP, and VAE, all the proposed ACNCL-based networks achieved better quantitative outcomes. Similarly, when compared with the results obtained in Table S1 for the brain CT dataset, the outcome had slight deviations, which did not affect the overall performance that reflected ACNCL-based techniques outperforming the single methods. In general, the quantitative evaluation confirmed our visual observations. As shown in Figure 13 below, ACNCL-U-Net achieved the best results with an SSIM of 0.899, indicating superior structural similarity, a low RMSE of 0.056, which suggests a minimal error in the denoised image, and a high PSNR of 28.3 dB, reflecting better overall image quality. The next-best-performing model was ACNCL-CA-AGF, which also outperformed all reference methods. In comparison, the best-performing reference model, WGAN-GP, had a lower SSIM of 0.845, a higher RMSE of 0.068, and a PSNR of 27.0 dB. These findings indicate that the ACNCL-based networks are more effective in denoising and preserving key image features than the reference methods. On the other hand, Figure S1 in the Supplementary Material shows that ACNCL-U-Net had the best performance at SSIM of 0.897, RMSE of 0.056, and PSNR of 28.6 db. There was a slight deviation in the SSIM outcome of 0.002, whereby Figure 13 (lung dataset) shows 0.899 and Figure S1 (brain dataset) shows 0.897. PSNR also deviated at 0.3, whereby Figure 13 shows a PSNR of 28.3 dB, and Figure S1 shows 28.6 dB. Although there was a slight deviation in the resultant outcome for SSIM and PSNR values, the general performance revealed that ACNCL-based methods had better results than the single methods for both lung datasets and brain datasets. (b) Results on MRI Dataset: For the MRI dataset, the reference methods included NLM [4], U-Net [11], DnCNN [13], and SCNN [14]. Table 6 shows quantitative results for MRI denoising for full cross-validation. As shown in Figure 14 below, the yellow parts indicated tumorous regions identified. All deep learning methods displayed better results, but NLM, while effective at low-to-moderate noise levels, struggled with higher noise levels, leading to over-smoothing of the images. This resulted in the loss of important structural details, especially in areas with subtle contrasts, which is critical for accurate diagnosis. Although the SCNN performed reasonably well, it introduced some noise, which could compromise the diagnostic quality  
Figure 12: Comparison of different methods for low-dose CT denoising on case T521. There is a metastatic tumor in the lungs. (**a**) Full-dose CT; (**b**) 25% dose CT; (**c**) BM3D; (**d**) RED-CNN; (**e**) WGAN-GP; (**f**) VAE; (**g**) ACNCL-U-Net; (**h**) ACNCL-DnCNN; (**i**) ACNCL-DenseNet; (**j**) ACNCL-CA-AGF; (**k**) ACNCL-DWT. The yellow parts indicate tumorous regions. The water images and the fat images of the WFI (Water-Fat Imaging) sequence were selected for visual comparison of MRI denoising, as illustrated in Figure 14.  
The findings in Figure 15 below reveal that ACNCL-based models outperformed their reference models due to the incorporation of Adversarial Content-Noise Complementary Learning. The ACNCL-DnCNN achieved a higher SSIM value of 0.893, indicating better preservation of structural similarity between the denoised image and the ground truth, as well as a lower RMSE of 0.059, suggesting reduced error in the denoised image. Additionally, it showed a higher PSNR value of 28.1 dB, reflecting better overall image quality compared to the reference DnCNN model, which had an SSIM of 0.829, RMSE of 0.071, and PSNR of 26.6 dB. These improvements suggest that ACNCL enhances the model's ability to handle noise while maintaining key image features more effectively. Figure 14: Comparison of different methods for under-sampling MRI denoising and tumor detection. (**a**) Full-sampling MRI (**b**) Four-fold under-sampling MRI. (**c**) NLM. (**d**) SCNN. (**e**) U-Net. (**f**) DnCNN. (**g**) ACNCL-U-Net. (**h**) ACNCL-DnCNN. (**i**) ACNCL-DenseNet.