Deep Learning-Based Ensemble Model for Automated Severity Assessment of Osteoarthritis from Medical Images

This work aims to solve the difficulty of recognizing knee osteoarthritis using deep learning methodologies. Conventional methods of manual classification of osteoarthritis severity tend to result in an error when rated according to the Kellgren-Lawrence (KL) degree a situation that complicates its utility in clinical practice. To address this problem, we are suggesting a strong framework using the most recent deep neural network approaches, namely InceptionV3 and EfficientNetB4, to improve the classification performance. In our study, we examine the performance of each model separately, defining the goal as identifying the correct Kellgren-Lawrence grade for the X-ray images of patients. We then explore how the models could be combined in an ensemble to gain the advantage of the strengths of both in their prediction task and enhance the accuracy. The results of our experiments show that the combined approach not only improves classification performance but also minimizes the risk of classification errors. The goal of this research is to present clinicians with a dependable tool in the form of deep learning methodologies to help diagnose knee osteoarthritis and ultimately improve the quality of treatment, thereby improving the patient’s quality of life.