Enabling Zero-Trust Architecture using a Federated Learning Framework with Differential Privacy and Blockchain Algorithms

In medical imaging, noise and artifacts can significantly degrade the quality and accuracy of diagnostic results. Traditional denoising techniques often struggle to balance noise reduction and preservation of critical details, leading to potential diagnostic errors. This paper proposes a novel deep learning-based framework, DL-DeNoiseNet, designed to effectively remove noise and reduce artifacts in medical images while preserving crucial anatomical and pathological information. The proposed framework leverages a combination of Convolutional Neural Networks (CNNs) and Residual Learning to model complex noise distributions and artifact patterns in medical images. A multi-scale feature extraction module is incorporated to capture fine-grained details across different resolutions, and a skip connection mechanism is employed to prevent loss of critical information during the denoising process. The performance of the proposed method is evaluated using standard medical imaging datasets, demonstrating superior results in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) compared to conventional and state-of-the-art denoising methods. The framework's robustness and efficiency make it a valuable tool for enhancing the quality of medical image diagnostics, ultimately contributing to improved clinical outcomes and decision-making. The proposed DL-DeNoiseNet framework was extensively evaluated on multiple publicly available medical imaging datasets, including MRI, CT, and ultrasound images, to benchmark its performance against existing state-of-the-art denoising methods. The evaluation was conducted using three primary metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Absolute Error (MAE). On the MRI dataset, the DL-DeNoiseNet achieved an impressive PSNR of 38.57 dB, significantly outperforming the conventional method (34.20 dB) and the state-of-the-art approach (36.45 dB). Additionally, the framework attained a high SSIM score of 0.920, reflecting a notable improvement over the conventional method (0.860) and the state-of-the-art (0.900). Similarly, the Mean Absolute Error (MAE) was minimized to 0.017, indicating superior noise reduction compared to the conventional method (0.034) and state-of-the-art (0.021). On the CT dataset, DL-DeNoiseNet demonstrated exceptional performance with a PSNR of 41.12 dB and an SSIM of 0.935, surpassing both the conventional method (PSNR: 37.80 dB, SSIM: 0.880) and the state-of-the-art approach (PSNR: 39.50 dB, SSIM: 0.915).