## 4 Results
### Ablation Studies  
To gauge the performance of the Adversarial Content-Noise Complementary Learning (ACNCL) denoising model for image denoising and tumor detection, we conducted a comprehensive ablation study on a CT image dataset of lung adenocarcinoma from The Cancer Genome Atlas (TCGA-LUAD) sourced from Kaggle. As shown in Table 4, the analysis compared ACNCL-enhanced networks with baseline models that employed a sole predictor, assessing improvements under different configurations. Specifically, ACNCL-U-Net was compared with the sole U-Net predictor that only focused on content learning and the sole U-Net predictor that only focused on noise learning. The comparative results revealed that ACNCL-U-Net outperformed both baseline models, showcasing its ability to effectively balance content and noise learning for enhanced diagnostics. A broad U-Net with enhanced convolutional layers for content learning and a broad U-Net with enhanced convolutional layers for noise learning were also compared. The findings indicated that the ACNCL-U-Net maintained its outstanding performance, demonstrating a robust architecture that leverages both content and noise features more effectively than the broader U-Net variants. Comparatively, ACNCL-DenseNet was compared with the sole DenseNet predictor focused on content learning and the sole DenseNet predictor focused on noise learning. Additionally, ACNCL-CA-AGF was compared with the sole CA-AGF predictor focused on content learning and the sole CA-AGF predictor focused on noise learning. Separately, the ACNCL-DWT was compared with a sole DWT predictor focused exclusively on content learning and one DWT predictor focused on noise learning. The training followed the same GAN model outlined in Figure 3, except that the generator was adjusted for each network. Table 4 provides quantitative results from an ablation study conducted on a CT dataset, comparing different variations in neural network architectures used for denoising and tumor detection. The key metrics used in this table to assess the efficiency of each model include SSIM, Root MSE, and PSNR. Higher SSIM (Mean \(\pm\) SD) values indicate better similarity, where a score closer to 1.0 represents a more accurate restoration of the image structure. The RMSE (Mean \(\pm\) SD) measures the average squared difference between pixel values of the predicted denoised image and the ground truth. Lower RMSE values indicate better performance. Finally, the PSNR represented in decibels (dB, Mean \(\pm\) SD) is a measure of the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Higher PSNR values, measured in decibels (dB), indicate better image quality, as less noise is present, signifying accurate tumor detection. The ACNCL-U-Net is a variation of the U-Net architecture incorporating ACNCL with the highest SSIM and PSNR among all methods. It has the highest SSIM (0.91) and PSNR (30.3 dB) among all the models, indicating it retains the most structure and clarity after denoising, with a relatively low RMSE (0.050), showing accurate restoration and improved tumor detection. Similarly, ACNCL-CA-AGF architecture incorporates Context-Aware-AGF mechanisms with an SSIM of 0.88 and PSNR of 29.9 dB, making it a strong candidate but still lagging behind the ACNCL-U-Net. The ACNCL-DnCNN architecture incorporates ACNCL with an SSIM of 0.86, PSNR of 28.9 dB, and RMSE of 0.056, respectively. It is slightly behind the ACNCL-CA-AGF but still very competitive. The ACNCL-DenseNet model uses the DenseNet architecture combined with ACNCL. It performs well with a high SSIM (0.85) and PSNR (28.1 dB), indicating more effective denoising than ACNCL-DWT with an SSIM value of 0.80 and PSNR of 26.9 dB. It is slightly behind the ACNCL-DnCNN in terms of performance.