Optimising Deep Learning Approaches for Orthopedic Imaging Disease Diagnosis: Comparative Analysis for Disease Classification

Abstract

Orthopedic conditions present a significant healthcare challenge, necessitating accurate and timely diagnosis to improve patient outcomes. Deep-learning techniques have transformed medical image analysis, providing exceptional accuracy in disease identification. This research presents a comprehensive comparative evaluation of state-of-the-art deep learning algorithms for diagnosing orthopedic conditions using medical imaging data. The study focused on classifying the conditions into fractured and non-fractured categories. The proposed model utilized a convolutional neural network (CNN) to extract features from the dataset, which were subsequently employed for bone classification. Because of image blurriness, the dataset underwent image equalization pre-processing. The model effectively distinguished between fractured and non-fractured bones, a critical aspect of accurate diagnosis. CNN achieved 98.7 % accuracy and 99% F1-score on a dataset of 4911 X-ray images, surpassing existing state-of-the-art approaches.

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