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@article{186163,
        author = {Dr. SONIA BAJAJ and ATHARV MANTRI and MANAS DIXIT and SHRAWNI KHANKE and BHAVINI VASTANI and BHUSHAN CHAUDHARI and DEVANSHI BHURE},
        title = {IMPROVING DIAGNOSTIC ACCURACY: A DEEP LEARNING APPROACH FOR BONE FRACTURE DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {6},
        pages = {4565-4569},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186163},
        abstract = {Bone Fractures Are Among the Most Frequent Medical Conditions, Often Requiring Accurate and Timely Diagnosis to Guide Treatment and Improve Patient Outcomes. Traditional Radiographic Interpretation, While Widely Used, Is Prone to Variability and Human Error, Particularly When Fractures Are Subtle or Obscured. Recent Advances in Deep Learning Have Opened New Opportunities for Automating Medical Image Analysis with Higher Precision and Consistency. This Study Explores the Integration of Radiological Expertise with Artificial Intelligence, Focusing on Convolutional Neural Networks (Cnns) For Automated Bone Fracture Detection in X-Ray Images. Our Approach Leverages Deep Feature Extraction from Pre-Trained Cnn Architectures, Followed by Classification Strategies Tailored for Fracture Identification. The System Is Designed to Enhance Diagnostic Accuracy, Reduce Interpretation Time, And Provide Reliable Decision Support to Clinicians. Experimental Results on Curated X-Ray Datasets Demonstrate Competitive Performance, Achieving Accuracy Levels Comparable to Or Exceeding Existing Methods, With Particular Strength in Handling Complex or Ambiguous Cases. By Bridging The Gap Between Radiologists and Deep Learning Models, This Research Underscores the Potential of Ai-Driven Diagnostic Tools to Augment Clinical Workflows, Minimize Diagnostic Errors, And Ultimately Improve Patient Care.},
        keywords = {Bone fracture detection, X-ray imaging, deep learning, convolutional neural networks, computer-aided diagnosis.},
        month = {March},
        }