Search Results (3,628)

Multi-exposure image fusion (MEF) aims to generate a well-exposed image by combining multiple photographs captured at different exposure levels. However, deep learning-based approaches are often highly dependent on the quality of the training data, which can lead to inconsistent color reproduction and loss of fine details. To address this issue, this study proposes a variance-driven hybrid MEF framework based on a U-Net architecture, which adaptively balances structural and chromatic information. In the proposed method, the variance of randomly cropped patches is used as a training weight, allowing the model to emphasize structurally informative regions and thereby preserve local details during the fusion process. Furthermore, a fusion strategy based on the geometric color distance, referred to as the Chroma scale, in the LAB color space is applied to preserve the original chroma characteristics of the input images and improve color fidelity. Visual gamma compensation is also employed to maintain perceptual luminance consistency and synthesize a natural fine image with balanced tone and smooth contrast transitions. Experiments conducted on 86 exposure pairs demonstrate that the proposed model achieves superior fusion quality compared with conventional and deep-learning-based methods, obtaining high JNBM (17.91) and HyperIQA (70.37) scores. Overall, the proposed variance-driven U-Net effectively mitigates dataset dependency and color distortion, providing a reliable and computationally efficient solution for robust MEF applications. Full article

Early diagnosis and rapid treatment of respiratory abnormalities such as many lung diseases including pneumonia, TB, cancer, and other pulmonary problems depend on accurate and fast classification of chest X-ray images. Delayed diagnosis and insufficient treatment lead to the subjective, labour-intensive, error-prone features of current manual diagnosis systems. To tackle this pressing healthcare issue, this work investigates many deep convolutional neural network (CNN) architectures including VGG16, VGG19, ResNet50, InceptionV3, Xception, DenseNet121, NASNetMobile, and NASNet Large. LungVisionNet (LVNet) is an innovative hybrid model proposed here that combines MobileNetV2 with multilayer perceptron (MLP) layers in a unique way. LungVisionNet outperformed previous models in accuracy 96.91%, recall 97.59%, precision, specificity, F1-score 97.01%, and area under the curve (AUC) measurements according to thorough examination on two publicly available datasets including various chest abnormalities and normal cases exhibited. Comprehensive evaluation with an independent, real-world clinical dataset from King Hussein Cancer Centre (KHCC), which achieved 95.3% accuracy, 95.3% precision, 78.8% recall, 99.1% specificity, and 86.4% F1-score, confirmed the model’s robustness, generalizability, and clinical usefulness. We also created a simple mobile application that lets doctors quickly classify and evaluate chest X-ray images in hospitals, so enhancing clinical integration and practical application and supporting fast decision-making and better patient outcomes. Full article

Background: Chronic total occlusion percutaneous coronary intervention (CTO PCI) is a complex revascularization procedure requiring advanced techniques to ensure procedural success and safety. Hybrid strategies combining antegrade dissection/re-entry (ADR) and retrograde approaches have become increasingly adopted in contemporary practice. Objectives: To systematically review and synthesize evidence comparing outcomes of ADR and retrograde CTO PCI techniques, with pooled estimates of success rates and adverse events. Methods: This review followed PRISMA 2020 guidelines. We searched PubMed, Cochrane CENTRAL, and Google Scholar for studies published between January 2015 and June 2025. Eligible studies included randomized controlled trials and observational studies reporting outcomes of ADR and/or retrograde CTO PCI. Data extraction was performed by two independent reviewers. Risk of bias was assessed using the Newcastle–Ottawa Scale and the Cochrane RoB 2.0 tool. A random-effects meta-analysis was conducted for consistently reported outcomes. Results: Twenty studies encompassing over 87,000 CTO PCI procedures were included. Pooled analysis of 16 studies demonstrated a technical success rate of 83.4% and a procedural success rate of 84.6%. The in-hospital major adverse cardiac event (MACE) rate was 3.3%. Hybrid strategies integrating ADR and retrograde approaches yielded the highest success rates (86–91%) with acceptable safety profiles. Use of adjunctive tools such as IVUS, dual arterial access, and re-entry devices was associated with improved outcomes. Discussion: Hybrid CTO PCI techniques are safe, effective, and reproducible across diverse clinical settings. When performed by experienced operators using modern adjuncts, these strategies provide durable benefits and should be considered standard for complex occlusions. Limitations include variation in study quality, heterogeneous procedural definitions, and lack of long-term data in several cohorts. Full article

Intravascular imaging has revolutionized the assessment and management of coronary artery disease, providing unparalleled insights into plaque morphology, lesion severity, and percutaneous coronary intervention (PCI) optimization. This comprehensive review explores the current landscape of intravascular imaging, detailing the principles and clinical utility of intravascular ultrasound (IVUS) and optical coherence tomography (OCT). We discuss the role of these technologies in various clinical scenarios, ranging from stable coronary artery disease to acute coronary syndromes, emphasizing their ability to refine diagnostic accuracy and therapeutic decision-making. A key focus is placed on their application in identifying vulnerable plaques, a critical step in preventing adverse cardiovascular events. Furthermore, we highlight the role of intravascular imaging in guiding PCI, improving stent deployment, and reducing procedural complications. Finally, we explore emerging imaging modalities and technological advancements poised to further enhance coronary assessment, including hybrid imaging techniques. In addition to established modalities, this review examines emerging imaging technologies and the growing integration of artificial intelligence (AI) and hybrid imaging systems, which hold promise for automated plaque characterization, improved reproducibility, and enhanced decision support during PCI. By summarizing the latest evidence and future directions, this review aims to provide a comprehensive reference for clinicians and researchers seeking to optimize the use of intravascular imaging in contemporary cardiovascular practice. Full article

Background/Objectives: Convolutional Neural Networks (CNNs) and advanced image pre-processing can enhance the classification of antioxidant effects applied on zebrafish intestine. This study proposes a hybrid technique that combines four deep learning (DL) models: the pre-trained Xception CNN, a custom-built autoencoder, a custom-built CNN, and a Vision Transformer (ViT). Methods: For classification features generated by DL models, the Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were proposed. Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the mentioned DL artificial intelligence (AI) algorithms were applied to improve the accuracy of the classification of histological images of zebrafish intestinal morphology. Results: In a binary classification, the following classes were studied on zebrafish intestine: (i) control and experimental-induced oxidative stress (OS); (ii) OS versus OS and theobromine (TB); and (iii) OS versus OS and caffeine (CAF). The novelty of the research lies in applying CLAHE to enhance image quality and utilizing four hybrid models to improve classification accuracy compared to raw images, when a private dataset of zebrafish intestine histology under certain chemical treatments (OS, TB, CAF) was employed. Conclusions: The best results are obtained in a binary classification with a hybrid combination of Xception and SVM for OS versus OS and TB classes, with an accuracy of 84.6% for pre-processed images, better than raw images, when the accuracy was 78.4%. Full article

Background: Locally advanced cervical cancer (LACC) represents a significant challenge in oncology, requiring accurate assessment of local extent and metastatic spread. Multiparametric magnetic resonance imaging (mpMRI) has assumed a central role in the loco-regional characterization of the tumor due to its high soft-tissue resolution and the ability to integrate functional information. Objectives: In this narrative review, we explore the use of mpMRI in the diagnosis, staging, and treatment response of LACC, comparing its performance with that of PET/CT, which remains complementary for remote staging. The potential of whole-body magnetic resonance imaging (WB-MRI) and hybrid PET/MRI techniques is also analyzed, as well as the emerging applications of radiomics and artificial intelligence. The paper also discusses technical limitations, interpretative variability, and the importance of protocol standardization. The goal is to provide an updated and translational summary of imaging in LACC, with implications for clinical practice and future research. Methods: Prospective and retrospective studies, systematic reviews, and meta-analyses on adult patients with cervical cancer were included. Results: Fifty-two studies were included. MRI demonstrated a sensitivity and specificity greater than 80% for parametrial and bladder invasion, but limited sensitivity (45–60%) for lymph node disease, lower than PET/CT. Multiparametric MRI was useful in early prediction of response to chemotherapy and radiotherapy and in distinguishing residual disease from fibrosis. The integration of MRI into Image-Guided Adaptive Brachytherapy (IGABT) resulted in improved oncological outcomes and reduced toxicity. The applications of radiomics and AI demonstrated enormous potential in predicting therapeutic response and lymph node status in the MRI study, but multicenter validation is still needed. Conclusions: MRI is the cornerstone of the local–regional staging of advanced cervical cancer; it has become an essential and crucial tool in treatment planning. Its use, combined with PET/CT for lymph node assessment and metastatic disease staging, is now the standard of care. Future prospects include the use of whole-body MRI and the development of predictive models based on radiomics and artificial intelligence. Full article

Magnetic Particle Imaging (MPI) is a new type of tracer-based imaging that has great spatial and temporal resolution, does not require ionizing radiation, and can see deep into tissues by directly measuring the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs). Unlike Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), MPI has very high contrast and quantitative accuracy, which makes it perfect for use in dynamic cardiovascular applications. This study presents a full picture of the most recent changes in cardiac MPI, such as the physics behind Field-Free Point (FFP) and Field-Free Line (FFL) encoding, new ideas for tracer design, and important steps in the evolution of scanner hardware. We discuss the clinical relevance of cardiac MPI in visualizing myocardial perfusion, quantifying blood flow, and guiding real-time interventions. A hybrid imaging workflow, which improves anatomical detail and functional assessment, is utilized to explore the integration of MPI with complementary modalities, particularly MRI. By consolidating recent preclinical breakthroughs and highlighting the roadmap toward human-scale implementation, this article underscores the transformative potential of MPI in cardiac diagnostics and image-guided therapy. Full article

Intravascular imaging (IVI), particularly intravascular ultrasound (IVUS) and optical coherence tomography (OCT), addresses the intrinsic limitations of two-dimensional coronary angiography by offering high-resolution information regarding vessel and plaque morphology before percutaneous coronary intervention (PCI) as well as enabling accurate assessment of stent expansion and apposition after implantation. These anatomical insights can translate into improved procedural success and late clinical outcomes. The magnitude of benefit appears closely related to lesion morphology and procedural complexity. While angiographic guidance may be sufficient in straightforward anatomies, IVI assumes a pivotal role in complex disease subsets. IVUS, with its deeper tissue penetration, real-time imaging capability, and lack of need for contrast flushing, is particularly advantageous for large-vessel interventions, chronic total occlusions, and contrast-sparing strategies. In contrast, OCT, offering superior axial resolution, excels in characterizing plaque composition and in detecting stent-related complications. Hybrid IVUS-OCT catheters have the potential to integrate the complementary strengths of both IVI modalities, thereby streamlining procedural workflows and broadening clinical applicability. Although current guidelines endorse IVI use in anatomically complex coronary artery disease, real-world adoption remains low, largely influenced by operator proficiency, regional differences, and reimbursement arrangements. Further research is warranted to identify lesion subsets in which one modality confers clear clinical benefit and to delineate the threshold of procedural complexity at which IVI becomes cost-effective. Full article

Age-related macular degeneration (AMD) is a leading cause of irreversible blindness, yet current tools for forecasting treatment outcomes remain limited by either the opacity of deep learning or the rigidity of rule-based systems. To address this gap, we propose a hybrid neuro-symbolic and large language model (LLM) framework that combines mechanistic disease knowledge with multimodal ophthalmic data for explainable AMD treatment prognosis. In a pilot cohort of ten surgically managed AMD patients (six men, four women; mean age 67.8 ± 6.3 years), we collected 30 structured clinical documents and 100 paired imaging series (optical coherence tomography, fundus fluorescein angiography, scanning laser ophthalmoscopy, and ocular/superficial B-scan ultrasonography). Texts were semantically annotated and mapped to standardized ontologies, while images underwent rigorous DICOM-based quality control, lesion segmentation, and quantitative biomarker extraction. A domain-specific ophthalmic knowledge graph encoded causal disease and treatment relationships, enabling neuro-symbolic reasoning to constrain and guide neural feature learning. An LLM fine-tuned on ophthalmology literature and electronic health records ingested structured biomarkers and longitudinal clinical narratives through multimodal clinical-profile prompts, producing natural-language risk explanations with explicit evidence citations. On an independent test set, the hybrid model achieved AUROC 0.94 ± 0.03, AUPRC 0.92 ± 0.04, and a Brier score of 0.07, significantly outperforming purely neural and classical Cox regression baselines (p ≤ 0.01). Explainability metrics showed that >85% of predictions were supported by high-confidence knowledge-graph rules, and >90% of generated narratives accurately cited key biomarkers. A detailed case study demonstrated real-time, individualized risk stratification—for example, predicting an >70% probability of requiring three or more anti-VEGF injections within 12 months and a ~45% risk of chronic macular edema if therapy lapsed—with predictions matching the observed clinical course. These results highlight the framework’s ability to integrate multimodal evidence, provide transparent causal reasoning, and support personalized treatment planning. While limited by single-center scope and short-term follow-up, this work establishes a scalable, privacy-aware, and regulator-ready template for explainable, next-generation decision support in AMD management, with potential for expansion to larger, device-diverse cohorts and other complex retinal diseases. Full article

Radiogenomics, which integrates imaging phenotypes with genomic profiles, enhances diagnosis, prognosis, and treatment planning for glioblastomas. This study specifically establishes a correlation between radiomic features and MGMT promoter methylation status, advancing towards a non-invasive, integrated diagnostic paradigm. Conventional genetic analysis requires invasive biopsies, which cause delays in obtaining results and necessitate further surgeries. Our methodology is twofold: First, an enhanced U-Net model segments brain tumor regions with high precision (Dice coefficient: 0.889). Second, a hybrid classifier, leveraging the complementary features of EfficientNetB0 and ResNet50, predicts MGMT promoter methylation status from the segmented volumes. The proposed framework demonstrated superior performance in predicting MGMT promoter methylation status in glioblastoma patients compared to conventional methods, achieving a classification accuracy of 95% and an AUC of 0.96. These results underscore the model’s potential to enhance patient stratification and guide treatment selection. The accurate prediction of MGMT promoter methylation status via non-invasive imaging provides a reliable criterion for anticipating patient responsiveness to alkylating chemotherapy. This capability equips clinicians with a tool to inform personalized treatment strategies, optimizing therapeutic efficacy from the outset. Full article

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective and objective weighting through a hybrid approach, where natural language processing (NLP) techniques extract perceptual preferences from user reviews, and the Interval-valued neutrosophic set analytic hierarchy process (IVNS-AHP) and entropy weight method (EWM) are employed to determine subjective and objective weights, respectively. The combined weights are used within the IVNS-VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) framework, enhanced by a possibility distribution (PD) to improve discriminative capability. Experiments were conducted using multiple performance metrics, including Precision, Recall, mean Average Precision at IoU = 0.5 (mAP@50), F1 Score, frames per second (FPS), and Parameters, to evaluate mainstream scalp detection algorithms. The results demonstrate that YOLOv8n achieves the highest comprehensive ranking with strong stability across different decision preferences. Comparative analyses with TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and fuzzy VIKOR variants confirm that the proposed PD-VIKOR method provides superior ranking stability and discriminative precision, offering a more reliable and robust evaluation under uncertainty. Full article

Tomato is a globally significant horticultural crop with substantial economic and nutritional value. High-precision phenotypic analysis of tomato fruit characteristics, enabled by computer vision and image-based phenotyping technologies, is essential for varietal selection and automated quality evaluation. An intelligent detection framework for phenomics analysis of tomato fruits was developed in this study, which combines image processing techniques with deep learning algorithms to automate the extraction and quantitative analysis of 12 phenotypic traits, including fruit morphology, structure, color and so on. First, a dataset of tomato fruit section images was developed using a depth camera. Second, the SegFormer model was improved by incorporating the MLLA linear attention mechanism, and a lightweight SegFormer-MLLA model for tomato fruit phenotype segmentation was proposed. Accurate segmentation of tomato fruit stem scars and locular structures was achieved, with significantly reduced computational cost by the proposed model. Finally, a Hybrid Depth Regression Model was designed to optimize the estimation of optimal depth. By fusing RGB and depth information, the framework enabled efficient detection of key phenotypic traits, including fruit longitudinal diameter, transverse diameter, mesocarp thickness, and depth and width of stem scar. Experimental results demonstrated a high correlation between the phenotypic parameters detected by the proposed model and the manually measured values, effectively validating the accuracy and feasibility of the model. Hence, we developed an equipment automatically phenotyping tomato fruits and the corresponding software system, providing reliable data support for precision tomato breeding and intelligent cultivation, as well as a reference methodology for phenotyping other fruit crops. Full article

Speech and language offer a rich, non-invasive window into brain health. Advances in machine learning (ML) have enabled increasingly accurate detection of neurodevelopmental and neurodegenerative disorders through these modalities. This paper envisions the future of ML in the early detection of neurodevelopmental disorders like autism spectrum disorder and attention-deficit/hyperactivity disorder, and neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease, through speech and language biomarkers. We explore the current landscape of ML techniques, including deep learning and multimodal approaches, and review their applications across various conditions, highlighting both successes and inherent limitations. Our core contribution lies in outlining future trends across several critical dimensions. These include the enhancement of data availability and quality, the evolution of models, the development of multilingual and cross-cultural models, the establishment of regulatory and clinical translation frameworks, and the creation of hybrid systems enabling human–artificial intelligence (AI) collaboration. Finally, we conclude with a vision for future directions, emphasizing the potential integration of ML-driven speech diagnostics into public health infrastructure, the development of patient-specific explainable AI, and its synergistic combination with genomics and brain imaging for holistic brain health assessment. Overcoming substantial hurdles in validation, generalization, and clinical adoption, the field is poised to shift toward ubiquitous, accessible, and highly personalized tools for early diagnosis. Full article

Benefiting from the advantages of unmanned aerial vehicle (UAV) platforms such as low cost, rapid deployment capability, and miniaturization, the application of UAV-borne synthetic aperture radar (SAR) has developed rapidly. Utilizing a self-developed monostatic Miniaturized SAR (MiniSAR) system and a bistatic MiniSAR system, our team conducted multiple imaging missions over the same vehicle equipment display area at different times. However, system disparities and time-varying factors lead to a mismatch between the distributions of the training and test data. Additionally, small ground vehicle targets under complex background clutter exhibit limited size and weak scattering characteristics. These two issues pose significant challenges to the precise detection of small ground vehicle targets. To address these issues, this article proposes a domain-unified adaptive target detection framework (DUA-TDF). The approach consists of two stages: image-to-image translation and feature extraction and target detection. In the first stage, a multi-scale detail-aware CycleGAN (MSDA-CycleGAN) is proposed to align the source and target domains at the image level by achieving unpaired image style transfer while emphasizing both global structure and local details of the generated images. In the second stage, a cross-window axial self-attention target detection network (CWASA-Net) is proposed. This network employs a hybrid backbone centered on the cross-window axial self-attention mechanism to enhance feature representation, coupled with a convolution-based stacked cross-scale feature fusion network to strengthen multi-scale feature interaction. To validate the effectiveness and generalization capability of the proposed algorithm, comprehensive experiments are conducted on both self-developed monostatic/bistatic SAR datasets and public dataset. Experimental results demonstrate that our method achieves an mAP50 exceeding 90% in within-domain tests and maintains over 80% in cross-domain scenarios, demonstrating exceptional and robust detection performance as well as cross-domain adaptability. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article