Enhanced Knee Joint Image Analysis Using Hybrid Machine Learning and Computer Vision Techniques

Abstract

This research aims to enhance orthopedic diagnosis accuracy by introducing a novel method for knee joint image processing that integrates computer vision and Machine Learning (ML) approaches. Traditional medical imaging analysis methods, while beneficial, often face limitations in precision and efficiency, particularly when interpreting complex knee pathologies. These limitations can lead to misdiagnoses or delayed treatments, which significantly impact patient outcomes. To address this issue, we propose a hybrid model that combines advanced computer vision techniques, such as Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), with Support Vector Machines (SVM) and Random Forests. The integration of SIFT and SURF allows for the extraction of robust and distinctive features from knee joint images, which are crucial for accurate classification. SVM and Random Forest algorithms are then employed to classify these features, providing a powerful mechanism to distinguish between healthy and pathological conditions. We utilize an extensive collection of knee images, including MRIs, CT scans, and X-rays, to train and optimize the model, ensuring it can handle a variety of imaging modalities and conditions. Our preliminary results demonstrate that the hybrid model surpasses traditional methods in terms of accuracy, precision, and efficiency. The enhanced performance of the hybrid model highlights its potential as a transformative tool in medical imaging, offering more reliable diagnostic outputs. Moreover, this research opens new avenues for improving diagnostic processes in orthopedics by reducing the reliance on manual image interpretation and enabling more consistent and objective assessments.