Fracture Detection using MobileNet Model

The use of the MobileNet model for fracture detection in medical imaging especially X-rays is investigated in this research article. Using MobileNet's lightweight design and efficiency, it's sought to develop a real-time diagnostic tool fit for use on cell phones and in resource-limited settings. Under strict preprocessing and enrichment to improve its resilience, the model was developed on a large annotated X-ray picture dataset and assessed. Training accuracy peaked at 100% in the sixth epoch and sustained a high accuracy of 98.44% in the tenth epoch according to findings of training and validation. Training loss concurrently showed a significant drop from 0.1427 at two epochs to 0.0282 at ten epochs. Test loss dropped dramatically from 0.4171 at two epochs to 0.0964 at ten epochs; test accuracy showed increasing improvement, rising to 97.22% at the tenth epoch. These findings emphasize the possible real-world clinical uses of the MobileNet model because they show its capacity to reach great accuracy with minimal rates of error in fracture identification. Emphasizing the value of mobile-optimized deep learning models in improving healthcare accessibility and decision-making, this work adds to the developing area of artificial intelligence-driven diagnostics.