Enhancing Fracture Detection in X-Ray Images Using Deep Convolutional Neural Networks: A Comparative Study of Advanced Architectures

Abstract

Radiology is a medical field where quick and accurate diagnosis can have a huge impact on public health. Timely diagnosis can save the life of large number of patients. However, manual examination of X-rays is prone to errors. To eradicate this problem, we have implemented multiple deep learning models which are capable of finding differences between images and can reduce human made errors. Though this is not a new idea and a large number of studies have already been conducted regarding this issue, but still, there are some limitations. Our study aims to cover things which they have implemented and including new features to address these gaps and extending the research further. The objective of this study was to test and train multiple deep learning models on a dataset such that they commit near zero errors and can be trusted and used safely in real world. The dataset is a combination of 3 different datasets of x-ray images on which the models are trained with proper measures and techniques such that the margin of error in detecting fractures is reduced to near zero. The models involved in the study include several deep convolutional neural network architectures including DenseNet121, Xception, ResNet50, VGG16, MobileNetV2 and InceptionV3to automate the detection of fractures. The results show that the models performed very well during test, with accuracies ranging around 94-95% and AUC score of 0.98. The test results and further comparison also shows that models which included architectures of ResNet50 and Xception, have performed better than models of previous studies. This shows that machine learning can be implemented in fracture diagnosis with high reliability and accuracy.

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