International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Satish Dekka 4 3 (May - June) 2022 06 May-June enFracture DetectionDenseNetDeep LearningMedical ImagingX-ray Analysis Early and accurate detection of bone fractures is essential for effective medical diagnosis and treatment planning Traditional radiographic interpretation is often timeconsuming and dependent on the expertise of radiologists which can lead to human error and delayed diagnosis This study proposes a DenseNetbased deep learning system for automated fracture detection using medical imaging data such as Xrays DenseNet or Densely Connected Convolutional Network enhances feature propagation and mitigates the vanishing gradient problem by connecting each layer to every other layer in a feedforward manner The proposed model is trained and validated on a curated dataset of labeled bone images where preprocessing techniques such as image normalization augmentation and noise reduction are applied to improve model robustness Performance evaluation metrics including accuracy precision recall and F1score are used to assess the systems diagnostic efficiency Experimental results demonstrate that the DenseNetbased model outperforms conventional convolutional neural networks CNNs in both accuracy and convergence speed The system provides a reliable and automated solution for assisting clinicians in fracture detection reducing diagnostic time and enhancing decisionmaking in emergency care Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2022 All Rights Reserved