## 7 Conclusions  
The proposed Adversarial Content-Noise Complementary Learning (ACNCL) model demonstrates significant advancements in medical image denoising and tumor detection for CT and MRI images. By utilizing a dual-predictor system, the ACNCL separates content preservation and noise suppression, allowing for optimized performance in both tasks. This approach integrates advanced techniques, including DnCNN for learning noise patterns while preserving anatomical structures, U-Net for spatial localization, ideal for segmentingtumors and preserving fine details, and DenseNet for deep feature extraction, enhancing hierarchical representations for robust noise removal and tumor differentiation, leading to improved accuracy and reliability in clinical diagnostics. The CA-AGF was used for context-aware filtering, and DWT for frequency-domain noise isolation. The quantitative results shown in Table 4 underscore its effectiveness, with the ACNCL-U-Net achieving an SSIM of 0.91, PSNR of 30.3 dB, and RMSE of 0.050 for CT images, surpassing standalone predictors like U-Net, which attained an SSIM of 0.88, PSNR of 29.0 dB, and RMSE of 0.055. Similarly, MRI image results reveal the ACNCL model's improved results over conventional methods such as NLM and SCNN, which exhibited limitations in handling non-uniform noise and preserving fine details.  
The model further demonstrates its robustness through an adversarial learning setup and online tumor image comparison for enhanced diagnostic accuracy. Adversarial learning ensures that denoised images resemble clean references, improving overall image quality and diagnostic reliability. The ablation studies shown in Figure 11 highlight the exceptional output of ACNCL-based networks, with ACNCL-U-Net achieving an SSIM of 0.979, PSNR of 31.353 dB, and RMSE of 0.032, compared to sole U-Net's SSIM of 0.862, PSNR of 28.653 dB, and RMSE of 0.035. Tumor detection accuracy improved notably after denoising, with clear visibility of tumor boundaries and higher structural fidelity in post-processed images, as demonstrated in Figures 6 and 7. Despite its significant contributions, the ACNCL model faces challenges such as computational complexity and reliance on external databases, which may pose data privacy concerns. Future research could focus on optimizing computational efficiency, improving data security, and expanding the model's application to other medical imaging modalities to further solidify its clinical impact.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/signals6020017/s1](https://www.mdpi.com/article/10.3390/signals6020017/s1)]([https://www.mdpi.com/article/10.3390/signals6020017/s1](https://www.mdpi.com/article/10.3390/signals6020017/s1)), Figure S1: Quantitative Results (Mean \(\pm\) SDs) Representation for Low-Dose brain CT Denoising on the Complete Test Set; Table S1: Brain CT performance for tumor detection Before (BDn) and after (ADn); Table S2: Brain MRI performance for tumor detection Before (BDn) and after (ADn).  
Conceptualization, T.A., methodology, T.A. and R.R., experimentation, T.A., original draft preparation, T.A. and R.R., review and editing, T.A. and G.O., funding acquisition, TA and G.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.