Application of Deep Learning in Medical Image Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 6
Special Issue Editors
Interests: machine learning; deep learning; big data; mobile analysis
Special Issues, Collections and Topics in MDPI journals
Interests: computer science; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
2. John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Interests: computer science; modeling and control of physiological systems; advanced non-linear control; human-computer interaction; physiological big data analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of deep learning (DL) has transformed medical image processing, enabling unprecedented accuracy in disease detection, segmentation, and diagnosis. State-of-the-art models such as convolutional neural networks (CNNs), transformers, and diffusion-based architectures are being applied to a range of medical modalities including MRI, CT, X-ray, ultrasound, and fundus imaging.
Particular emphasis has been placed on segmentation models—such as U-Net, L-Net, and 3D U-Net—which play a vital role in delineating anatomical structures and pathological regions, forming the foundation for quantitative clinical analysis. Furthermore, advances in pre-processing (e.g., noise reduction, normalization, contrast enhancement) and post-processing (e.g., morphological refinement, uncertainty estimation, ensemble optimization) techniques have substantially improved model performance and robustness.
For this Special Issue, we are seeking original research papers, reviews, and case studies that explore novel architectures, enhanced pre- and post-processing workflows, explainable AI approaches, and clinically validated segmentation frameworks. Submissions focusing on integrated and interpretable solutions that bridge technical innovation with clinical applicability are of particular interest.
Potential research topics include the following:
- Deep learning architectures for medical image analysis.
- Image segmentation, detection, and classification in healthcare.
- U-Net, L-Net, and 3D U-Net for biomedical image segmentation.
- Advanced pre-processing and post-processing methods.
- Transformer and diffusion-based medical imaging solutions.
- Explainable and trustworthy AI in clinical applications.
- Federated and privacy-preserving learning in healthcare.
- Multimodal and cross-domain medical data fusion.
- Computer-aided diagnosis and decision support systems.
Dr. Lehel Denes-Fazakas
Dr. László Szilágyi
Prof. Dr. Levente Adalbert Kovács
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- medical image processing
- medical imaging
- image segmentation
- disease detection
- convolutional neural networks (CNNs)
- transformers
- diffusion models
- U-Net
- L-Net
- 3D U-Net
- MRI
- CT
- X-ray
- ultrasound
- fundus imaging
- pre-processing
- post-processing
- noise reduction
- contrast enhancement
- morphological refinement
- uncertainty estimation
- ensemble learning
- explainable AI (XAI)
- trustworthy AI
- clinical validation
- computer-aided diagnosis (CAD)
- decision support systems
- multimodal learning
- cross-domain data fusion
- federated learning
- privacy-preserving AI
- biomedical image analysis
- quantitative clinical analysis
- segmentation frameworks
- medical data integration
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