## 5 Cross-Contextual Studies  
In functional imaging, noise affects the accuracy of quantitative measurements, leading to incorrect clinical interpretations. In cases where there are low-quality medical images, critical diagnoses might be misinterpreted entirely. Therefore, it is necessary to evaluate the generalization capability of a trained ACNCL predictor model. We investigated the universality of the proposed ACNCL-based networks using brain CT dataset object detection for a comparison of the obtained results with those of brain CT images. ACNCL was applied to the human brain CT dataset accessed at this link: [[https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data)]([https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data)), accessed on 13 December 2024. The full-dose and 25% dose CT images in the brain CT data were obtained from actual scans. The experiments represented in Figure 6 (CT lung dataset) and Figure 7 (MRI brain dataset) after denoising revealed that DnCNN had the best achievement based on PSNR, SSIM, and RMSE values compared with DenseNet, AGF, DWT, and U-NET across all CT and MRI datasets. Quantitatively, DnCNN in Table 2 for CT lung images had an SSIM of 0.64 before denoising (BDn) and moved up to an SSIM of 0.91 after denoising (ADn), RMSEs of 0.108 (BDn) and 0.048 (ADn), and finally PSNRs of 23.0 dB (BDn) and 29.5 dB (ADn). On the other hand, as shown in Table S1, the DnCNN using the brain CT dataset had SSIMs of 0.63 (BDn) and 0.90 (ADn), RMSEs of 0.112 (BDn) and 0.054 (ADn), and lastly PSNR values of 22.5 dB (BDn) and 28.7 dB (ADn). The same results for DnCNN's exceptional outcome over other methods are shown in Table S2 for the brain MRI dataset collected from [26]. Given that WGAN-GP achieved better performance among the reference methods over the CT lung dataset (TCGA-LUAD), as shown in Figure 13 and Table 5, it was compared with the output generated in Figure S1 and Table S1 for the brain CT dataset where WGAN-GP had exceptional PSNR and SSIM compared to the other reference methods. Notable improvement across different datasets for tumor detection after denoising was reported across the four datasets, as shown in Tables 2 and 3, S1 and S2. The results in Figure 13 and Table 5, put ACNCL-U-Net in the lead with an SSIM of 0.899, PSNR of 28.3 dB, and RMSE of 0.056, and generally, all other ACNCLs remarkably gave better results than all other reference methods. This is supported by the findings of the brain CT dataset in Figure S1, where notable achievements for ACNCL networks were seen compared to the reference methods. To evaluate the effectiveness of the Adversarial Content-Noise Complementary Learning (ACNCL) model for image denoising and tumor detection, we compared the ACNCL-based models with their baseline networks comprising DnCNN, CA-AGF, DenseNet, U-Net, and DWT which utilized a single predictor for each case, as shown in Table 4. The results reveal that ACNCL-based networks outperformed the sole predictors for content and noise prediction. For instance, ACNCL-U-Net had the best results across all the metrics used. It had an SSIM value of 0.91, PSNR value of 30.3 dB, and RMSE of 0.050 compared to the sole U-net for content prediction, with an SSIM of 0.88, PSNR of 29.0 dB, and RMSE of 0.055, and the sole U-net for noise prediction, with an SSIM of 0.87, PSNR of 28.4 dB, and RMSE of 0.056. The ACNCL-U-Net performance indicates that it was able to retain the most structures, image clarity, and restoration after denoising. This was followed by ANCL-CA-AGF and ACNCL-DnCNN, which lagged behind ACNCL-U-Net but were competitive and produced better results than ACNCL-DWT. All the sole predictors for noise and content performed distally compared to the ACNCL-based networks.