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.
