An Enhanced Approach to Detect the Eight Stages of Osteoarthritis Using Learning Algorithm

This paper introduces an innovative method in the classification of knee osteoarthritis based on an ensemble of convolutional neural networks specifically MobileNet and DenseNet. Osteoarthritis is a common degenerative joint disease that causes much pain, diminished mobility, and poor quality of life, primarily among the older population. Precise and timely diagnosis is an important aspect because it will only lead to successful intervention and the proper planning for treatment. Our approach uses an ensemble model with MobileNet and DenseNet. It significantly outperforms a traditional single-CNN approach for feature extraction in knee radiographs. The ensemble model captures the diverse architectural strengths, thereby catching a wide variety of image patterns while reducing overfitting due to training on different subsets of the dataset. In addition, more advanced ensemble strategies such as bagging and boosting improve the accuracy and robustness of the model. The method was validated using a large dataset of knee radiographs covering the full spectrum of osteoarthritis progression. Experimental results demonstrated higher classification accuracy than traditional techniques and, in some cases, outperformed expert radiologists. This research has shown the power of CNN ensembles in medical imaging, thus underlining the transformative role of machine learning in healthcare and its potential to support clinical diagnostics and improve patient outcomes.