AN END-TO-END ORTHOPEDIC DISEASE IMAGE CLASSIFICATION SYSTEM USING CONVOLUTIONAL NEURAL NETWORKS

Abstract

Orthopedic conditions such as fractures, arthritis, osteoporosis, and joint-related disorders are commonly evaluated using musculoskeletal X-ray images; however, traditional diagnosis relies on manual interpretation by orthopedic specialists, which can be time-consuming, subjective, and prone to human error, particularly in high-pressure clinical environments. Variations in patient anatomy, image quality, noise, and overlapping bone structures further increase diagnostic complexity. To address these challenges, this study presents an end-to-end automated orthopedic disease classification system based on Convolutional Neural Networks (CNNs). The proposed framework utilizes the publicly available MURA dataset, comprising labeled X-ray images from multiple upper limb regions representing both normal and abnormal conditions. Comprehensive data engineering techniques, including exploratory data analysis, image preprocessing, and data augmentation, are applied to enhance data quality and improve model generalization, with all images standardized to a fixed resolution and normalized prior to training. The CNN model is designed to automatically learn discriminative spatial features from X-ray images and classify them into different orthopedic disease categories. Model performance is evaluated using standard metrics such as accuracy, precision, recall and confusion matrix analysis. Experimental results demonstrate that the proposed approach effectively distinguishes between various orthopedic conditions, highlighting the potential of deep learning-based systems to improve diagnostic accuracy, reduce clinician workload, and support informed decision-making in orthopedic practice.