## 4 Results
### Ablation Studies
The results of these comparisons demonstrated that ACNCL models consistently outperformed their single-focus counterparts, highlighting the importance of integrating both content and noise learning for enhanced diagnostic accuracy. Figure 10 presents the results of the ablation study, whereby impressive analysis was realized when ACNCL-based networks were compared with the sole predictors. For example, a sole DenseNet predictor that only focused on content learning and achieved SSIM 0.80, PSNR 25.7 dB, and RMSE 0.072, and a sole DenseNet predictor that only focused on noise learning and achieved SSIM 0.81, PSNR 26.0 dB, and RMSE 0.070, were compared with ACNCL-DenseNet whose outcome outperformed the single predictors at an SSIM of 0.85, PSNR value of 28.1 dB, and RMSE of 0.058 (Figure 11). The results demonstrated that ACNCL enhances image denoising and tumor detection by using GANs to effectively differentiate between real and noisy images, improving noise removal and preserving fine details for the detection of tumors. Figure 11: _Cont._  
Comparison with Reference Methods  
(a) Results on CT Dataset: For the CT denoising task, we selected BM3D [17], REDCNN [15], Wasserstein GAN with Gradient Penalty (WGAN-GP) [19], and a variational autoencoder (VAE) [9] as reference methods in the CT denoising tasks. Two representative CT slices containing metastases from patients labeled T521 and T430 are part of the dataset used in the study, which were chosen to visualize the denoising performance of these  
Figure 11: Visual comparison in the ablation studies on case **T430** on CT dataset. The first row is the real image pair. The second and third rows are the ablation study for the situation where the predictor is U-Net. The fourth and fifth rows are the ablation study for DnCNN. The sixth and seventh rows are the ablation study for DenseNet, the eighth and ninth rows are the ablation study for CA-AGF, and lastly, the tenth and eleventh rows are the ablation studies for DWT. Noise images are calculated by subtracting corresponding predicted content images and the real full-dose image. methods, and the labels T521 and L430 were anonymized to refer to patients in clinical datasets while preserving their privacy. The CT slices are 2D images generated from 3D CT scans, where each slice represents a cross-sectional view of the patient's anatomy. In this case, the slices specifically contain metastases, which are cancerous growths that have spread from the original tumor to other areas of the body. In Figure 12, the yellow parts indicate tumorous regions. The BM3D shows blurriness and noise; although the RED-CNN can effectively reduce noise, it tends to overfit the training data, particularly when the dataset lacks diversity. This overfitting leads to poor generalization of unseen data and may introduce noise. The VAE produced denoised images that were blurry. This blurriness was due to the variational approach prioritizing smooth and continuous latent spaces, which can result in the loss of sharp edges and fine details in CT images. The WGAN-GP method excelled in producing high-quality images that retain fine details and textures. This method used Wasserstein distance to provide a smooth and reliable loss function and prevented mode collapse through reducing discriminator dominance for stable training. The gradient penalty was used to prevent training instability by ensuring that medical images remained diverse and realistic. It is obvious that compared with comparison methods, the ACNCL-based networks achieved better denoising outcomes in terms of both content preservation and noise suppression. Among the proposed ACNCL-based networks, ACNCL-U-Net and ACNCL-CA-AGF outperformed ACNCL-DnCNN, ACNCL-DenseNet, and ACNCL-DWT in the aspect of vascular structure preservation. Table 5, gives a summary of quantitative results in the whole test set for CT lung datasets. This table shows that ACNCL-U-Net and ACNCL-CA-AGF achieved more efficiency in PSNR and SSIM than ACNCL-DnCNN, ACNCL-DenseNet, and ACNCL-DWT.