ArticleScientific reports2024
Comparative analysis of vision transformers and convolutional neural networks in osteoporosis detection from X-ray images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Precision Nanotechnology: Revolutionizing Therapeutic Strategies Against Drug-Resistant Breast Cancer.Annals of biomedical engineering · 2026Review
- A Trustable Spine Abnormalities Classification System Using ResNet50 and VGG16 Supported by Explainable Artificial Intelligence.Biomimetics (Basel, Switzerland) · 2026Article
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs.Scientific reports · 2026Article
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.Frontiers in artificial intelligence · 2026Article
- Advancing Congenital Heart Defect Treatments: Synergistic Approaches with Stem Cells and Functional Scaffolds.Stem cell reviews and reports · 2026Review
- Enhanced diagnosis of osteoporosis using vision transformer with lumbar MRI.BMC medical imaging · 2025Article
- Lessons Learned from Liver-on-Chip Platform.Annals of biomedical engineering · 2025Review
- Role of Nuclear Receptors on the Progression of Multiple Sclerosis: A Review.Cellular and molecular neurobiology · 2025Review
- Modern therapeutic approaches for hepatic tumors: progress, limitations, and future directions.Discover oncology · 2025Review
- Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection.Biomolecules & biomedicine · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Dissociating physiological ripples and epileptiform discharges with vision transformers.bioRxiv : the preprint server for biology · 2025Article
- Mesenchymal stem cells and their exosomes: a novel approach to skin regeneration via signaling pathways activation.Journal of molecular histology · 2025Review
- Mesenchymal stromal cells in bone marrow niche of patients with multiple myeloma: a double-edged sword.Cancer cell international · 2025Review
- Application of novel strategies in chronic wound management with focusing on pressure ulcers: new perspective.Archives of dermatological research · 2025Review
- Navigating the challenges: ultrasound innovations in brain glioma surgery.Frontiers in neurology · 2025Review
- Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Within the scope of this investigation, we carried out experiments to investigate the potential of the Vision Transformer (ViT) in the field of medical image analysis. The diagnosis of osteoporosis through inspection of X-ray radio-images is a substantial classification problem that we were able to address with the assistance of Vision Transformer models. In order to provide a basis for comparison, we conducted a parallel analysis in which we sought to solve the same problem by employing traditional convolutional neural networks (CNNs), which are well-known and commonly used techniques for the solution of image categorization issues. The findings of our research led us to conclude that ViT is capable of achieving superior outcomes compared to CNN. Furthermore, provided that methods have access to a sufficient quantity of training data, the probability increases that both methods arrive at more appropriate solutions to critical issues.
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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 Socratic graph.