Machine Learning based Detection and Severity Classification of Knee Osteoarthritis

Knee Osteoarthritis (KOA) is a common musculoskeletal condition that has a significant worldwide influence on patient well-being. Despite its importance, manually detecting KOA poses tremendous obstacles. Traditional diagnostic methodologies heavily depend on the visual interpretation of radiographic images by certified radiologists. This approach leads to laborious assessments and introduces the potential for discrepancies among observers. Effective manual identification is further complicated by the intricate structure of the KOA grading system, which evaluates joint space narrowing, osteophyte production, and overall degeneration. This paper presents a method for addressing these challenges by integrating the capabilities of Deep Learning (DL) and Machine Learning (ML) models. The dataset utilized in this research was acquired from Kaggle and comprises image samples that have been assigned KOA grades between 0 to 4. The data source is utilized to generate two distinct datasets. The initial dataset is structured as a binary classification task, wherein images are categorized as 'KOA' (grades 2 to 4) or 'healthy' (grades 0 and 1). The second dataset is the same as the original dataset, with five classes for each KOA grade. An important objective of this research is to assess the performance of the model in both binary and multi-class classification. The datasets go through pre-processing phases such as segmentation and normalization before being fed to a CNN (Convolutional Neural Network) model to extract significant features. Following this, the gathered features are utilized to train and validate numerous ML models. The assessment is predicated on several critical metrics that give information about the effectiveness of the model. The results of our study indicate that the Support Vector Machine (SVM) demonstrates outstanding efficiency in the area of binary classification. In contrast, the Random Forest (RF) model performs exceptionally well in multi-class classification, yielding promising outcomes. The suggested method not only addresses the complications of KOA grading, but it also presents a viable technique for early diagnosis and severity categorization, hence assisting clinicians and lowering the diagnostic burden on primary radiologists.