Bone Fracture Detection using Deep learning

Bone fracture detection is a vital task in medical diagnostics, as accurate and timely identification of fractures can significantly impact patient treatment and recovery. Traditionally, analyzing X-ray images for fractures requires skilled radiologists and is subject to human limitations such as fatigue and workload, which can lead to missed or delayed diagnoses. With advancements in deep learning, there is growing potential to automate this process and enhance diagnostic accuracy. This project explored the use of deep learning models—specifically a custom Convolutional Neural Network (CNN) and a ResNet-18 architecture—to detect bone fractures in X-ray images. The custom CNN model, though effective to some extent, struggled with generalization and exhibited signs of overfitting. In contrast, ResNet-18 demonstrated a smoother learning curve and significantly better performance due to its deep architecture and residual learning capabilities.

The project also highlighted challenges such as dataset imbalance, which negatively affected the models’ ability to generalize across diverse cases. To address this, future work could include data augmentation, hyperparameter tuning, and the use of advanced architectures like VGG16, ResNet-50, InceptionV3, and MobileNet. Additionally, applying image preprocessing techniques such as Gaussian filtering and CLAHE can help improve image quality and model performance. This work demonstrates the promising role of deep learning in the medical field, particularly in assisting radiologists by providing initial assessments and reducing diagnostic time. With further development and refinement, such AI-assisted diagnostic tools can enhance healthcare delivery, especially in resource-limited settings, by offering faster, more reliable, and scalable fracture detection solutions.