Preserving Critical Signals in Magnetic Image Denoising: A Deep Learning Approach with Selective Feature Preservation

Muhammad Umair, Khalid Hamid — Mon, 01 Sep 2025 00:00:00 +0000

Noise contamination is a major issue in medical imaging because it affects the clarity of structures and can impact how doctors make diagnoses. To address this, this study introduces a new deep learning method called DenoiseNet. The main goal is to reduce noise without losing important details of the body’s anatomy, which is a challenge with traditional filtering techniques and standard CNN models that often smooth out too much and lose key information. DenoiseNet builds on the U-Net structure by adding spatial attention, channel attention, and residual blocks. These components help the model focus on noisy areas, highlight important features, and ensure that the learning process works smoothly. The model uses residual-attention fusion in the bottleneck, extracts important features in the encoder, and restores clear images in the decoder using skip connections and residual attention blocks. A hybrid loss function that combines MSE and SSIM helps balance pixel accuracy with how realistic the image looks, improving both noise reduction and structure preservation. Hybrid DenoiseNet, incorporating spatial and channel attention along with residual U-Net blocks, achieves a PSNR of 32.27 dB and SSIM of 0.9598. The performance is robust in both our Salt & Pepper noise dataset as well as a semi-synthetic MRI dataset—outperforming both BM3D (31.9 dB, 0.9862) and DnCNN (31.5 dB, 0.8826) under identical test conditions. These qualitative gains are a demonstration of improved noise suppression without loss of structural detail. This approach's strength is its capacity to produce encouraging outcomes even in the early phases of training, exhibiting consistent performance and the possibility of more gains with more time spent training. In comparison to conventional methods, the model gains improved feature representation and better convergence by including attention and residual learning into the U-Net backbone. When taking everything into account, the proposed DenoiseNet demonstrates that merging residual learning with attention mechanisms on a U-Net structure creates a powerful and effective approach for removing noise from medical images. The results show that the model preserves key anatomical details essential for accurate clinical analysis while also effectively reducing noise. These outcomes highlight DenoiseNet's potential as a robust framework that can be further improved and adapted for different types of medical imaging, paving the way for better patient outcomes and more reliable diagnoses.

Predictive Analysis on Project Management Success through AI

Muhammad Hamid Qureshi, Muhammad Usman Sattar — Mon, 01 Sep 2025 00:00:00 +0000

The modern business landscape is evolving rapidly, and each project comes with its own set of challenges and complexities. This calls for new and more inventive methods of handling such projects, as this area of work is becoming increasingly intricate and fluid. This study is centered on the predictive use of AI (Artificial Intelligence) technologies like ML (Machine Learning) and LLMs (Large Language Models) to ensure more effective project management at each of the ten PMBOK® knowledge areas. Merging the qualitative feedback from senior project managers and the quantitative KPIs—budget variance, schedule adherence, stakeholder satisfaction, and risk response time—from 84 organizations in construction, pharma, IT, finance, and manufacturing based in the EU, UK, USA, and Middle East provides richer insights. The analysis demonstrates how advanced AI tools, from predictive analytics to intelligent chatbots, streamline a project’s life cycle by enhancing efficiency, acuity, and overall decision-making. Predictive AI is demonstrated to bolster schedule and risk management as well as stakeholder interaction. Traditional metrics such as schedule creation and risk detection indicate a significant improvement for AI-supported projects, with 50% and 25% improvement respectively, as well as 30% higher stakeholder satisfaction.

Journal of Computing & Biomedical Informatics

Preserving Critical Signals in Magnetic Image Denoising: A Deep Learning Approach with Selective Feature Preservation

Predictive Analysis on Project Management Success through AI