Published Paper Details:

Bone fractures tend to be one of the commonest medical conditions that need prompt and accurate diagnosis for the right treatment and healing. The traditional methods for detecting fracture rely a lot on human errors and long manual analyses of X-ray images. Bone fracture detection forms a lot of essential diagnoses in this medical field, taking long time durations. This research proposes a machine learning system based on a deep CNN model to achieve automatic bone fracture identification. This convolutional neural network model will be trained on a large dataset of X-ray images to learn the patterns associated with the fracture. Training, testing, and validation comprise the three sections of the dataset. The efficiency of the suggested approach in detecting with high perfection is demonstrated by its 96.33% accuracy rate. When compared with traditional methods, the use of CNNs significantly decreases the amount of time needed for diagnosis and increases the overall accuracy of fracture identification. The system can precisely detect bone fractures thanks to the suggested model's influence on a deep CNN architecture that extracts characteristics from X-ray pictures. A widely utilized technique in image processing and computer vision, canny edge detection is frequently used in combination with CNNs to detect bone fractures. The dataset consists of medical photos with annotations that have been pre-processed for augmentation and normalization to increase the robustness of the model. By getting the AI based solutions integrated into clinical workflows, this research signifies the great role deep learning can play in the revolution of fracture diagnosis.