An Intelligent Knee Osteoarthritis Disease Prediction using Hybrid Deep Learning with Classification Principles

Knee osteoarthritis is a prevalent degenerative joint disease that significantly impacts patient mobility and quality of life.This study presents a novel hybrid deep learning approach that integrates a LeNet-based Convolutional Neural Network with a Random Forest classifier for predicting knee osteoarthritis severity. The model leverages robust feature extraction from knee X-ray images and couples it with ensemble classification to yield highly accurate predictions. Extensive cross-validation experiments demonstrated an overall accuracy of 93.3%, with sensitivity and specificity values of 92.0% and 93.9%, respectively, and an Area Under the Curve (AUC) of 0.95. These performance metrics underscore the model's strong discriminative power in differentiating between various osteoarthritis grades. The methodology incorporates meticulous data preprocessing, augmentation, and hyperparameter tuning to address the challenges posed by variations in image quality and patient demographics. The promising results indicate that the hybrid Lenet-RF model is a potent tool for early and reliable disease detection, potentially leading to improved clinical outcomes. Overall, this approach not only outperforms traditional standalone models but also offers a scalable solution for real-time clinical diagnostics, paving the way for its integration into routine healthcare practices.