ReviewSensors (Basel, Switzerland)2025
A Comprehensive Survey of Deep Learning Approaches in Image Processing.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
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- AI for Prognosis Among People Living With HIV: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Recent Advances in Image Processing and Computer Vision: Algorithms and Applications.Journal of imaging · 2026Article
- X-Ray Weld Image Detection Method of Water Injection Network Based on Sparse Representation.Sensors (Basel, Switzerland) · 2026Article
- YOLO11-Based Weld Defect Detection Method for X-Ray Images Integrating SIoU Bounding Box Regression and P2 Shallow Feature Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with Classification-Guided Adaptive Temporal Inference.Sensors (Basel, Switzerland) · 2026Article
- Comparative evaluation of XGBoost, TabNet, and FT transformer models for fatal crash prediction under extreme class imbalance.Scientific reports · 2026Article
- Optimizing Radiographic Diagnosis Through Signal-Balanced Convolutional Models.Journal of imaging · 2026Article
- A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.Biomedical engineering online · 2026Article
- Federated learning for privacy-preserving skin cancer classification using deep neural networks.Frontiers in oncology · 2026Article
- Artificial Intelligence integration and health system performance: effects on diagnostic accuracy, operational efficiency, and workforce outcomes in medical imaging departments.Frontiers in public health · 2026Article
- Basis-driven learnable operator for MLP-mixers.Frontiers in artificial intelligence · 2026Article
- Non-destructive identification of closely related japonica rice seeds using hyperspectral imaging combined with HHO-optimized XGBoost.Frontiers in plant science · 2026Article
- A hybrid deep boosting framework with adaptive label stabilization for SEM-based porosity estimation in fly-ash cement mortar.Frontiers in artificial intelligence · 2026Article
- Real-Time Panoramic Surveillance Video Stitching Method for Complex Industrial Environments.Sensors (Basel, Switzerland) · 2025Article
- Explainable deep learning ensemble framework for accurate classification of wild poisonous mushroom species.BMC biotechnology · 2025Article
- Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi.Biology · 2025Article
- Detection of Building Equipment from Mobile Laser Scanning Point Clouds Using Reflection Intensity Correction for Detailed BIM Generation.Sensors (Basel, Switzerland) · 2025Article
- Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data. Key advancements, such as techniques improving model efficiency, generalization, and robustness, are examined, showcasing DL's ability to address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous model evaluation are also discussed, underscoring the importance of performance assessment in varied application contexts. The impact of DL in image processing is highlighted through its ability to tackle complex challenges and generate actionable insights. Finally, this survey identifies potential future directions, including the integration of emerging technologies like quantum computing and neuromorphic architectures for enhanced efficiency and federated learning for privacy-preserving training. Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. These advancements are positioned to further extend the capabilities and applications of DL, driving innovation in image processing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.