ArticleDiagnostics (Basel, Switzerland)2021
Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- The Interface of Artificial Intelligence and the Electronic Medical Record in Orthopaedic Surgery: Current Applications and Future Directions.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Multimodal deep learning for bone tumor diagnosis with clinical imaging, pathology, and blood biomarkers.Journal of bone oncology · 2025Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025Review
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- A comprehensive deep learning approach to improve enchondroma detection on X-ray images.Scientific reports · 2025Article
- A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors.Scientific data · 2025Article
- Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study.Insights into imaging · 2024Article
- Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study.Journal of clinical medicine · 2023Article
- Deep Learning-Based Detection of Bone Tumors around the Knee in X-rays of Children.Journal of clinical medicine · 2023Article
- Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.Journal of digital imaging · 2023Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- A bi-directional deep learning architecture for lung nodule semantic segmentation.The Visual computer · 2022Article
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Article
- Osteolysis: A Literature Review of Basic Science and Potential Computer-Based Image Processing Detection Methods.Computational intelligence and neuroscience · 2021Review
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Authors and funding
4 authors.
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Abstract
Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.
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