Comparative Analysis of Deep Learning Models for Fracture Detection and Classification in X-ray Images

Abstract

For efficient medical diagnosis and treatment planning, which have historically relied on expert interpretation of radiographic images, accurate classification of bone fractures is essential. In this work, we introduce an automated method that uses machine learning techniques to improve fracture classification accuracy and efficiency. We use a large dataset with various bone fracture images to compare the effectiveness of two different models: Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN). By utilizing sophisticated preprocessing methods to maximize feature extraction and reduce noise, we thoroughly assess the models classification performance. Our results show a substantial difference in performance between the two models: the CNN model achieves an amazing classification accuracy of 94%, while the MLP model only manages 73%. This significant advancement highlights how well the CNN model can represent complex fracture patterns, highlighting its potential to transform orthopedic medicines diagnostic procedures and improve patient care. These machine learning algorithms present a promising option for improving treatment results for individuals with bone fractures by automating fracture categorization and lowering reliance on subjective human interpretation.