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The increasing demand for automated medical imaging analysis has led to advancements in deep learning for precise irregularity detection. The proposed FracTum system leverages YOLOv11n, a deep learning-based multiple object detection model, to detect bone fractures and tumors in X-ray images. The model is trained using two datasets to identify and highlight affected regions using bounding boxes accurately. A Streamlit-based web interface enables seamless user interaction, allowing X-ray uploads for real-time detection. If irregularities are found, they are marked with bounding boxes; otherwise, a message indicates no detection. The system ensures efficient, accurate, and scalable medical image analysis, enhancing early diagnosis and clinical decision-making.
Keywords:
Multiple-Object Detection, YOLOv11n, Fracture Detection, Tumor Detection, Deep Learning, Medical Image Analysis
Cite Article:
"FracTum: Bone Fracture and Tumor Detection using YOLOv11 Model", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c172-c177, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503229.pdf
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000289
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator