Abstract
Osteoarthritis (OA) of the knee is a common disorder that contributes to physical decline and activity limitation. Early OA diagnosis and treatment can stop the disease's progression. The assessment of a physician's visual examination is impartial, subject to different interpretations, and highly dependent on their level of experience. Therefore, a system that employs machine learning techniques to automatically determine the degree of knee OA has been proposed in this study. At first, a specifically created one stage YOLOv2 network is employed to estimate the size of the kneecap according to the distribution of knee joints in low contrast thermal images. To be more specific, the knee Kellgren-Lawrence (KL) grading assignment is ordinal; therefore, a harsher penalty is provided for misrepresentation with a larger gap between the anticipated and actual KL grade. A machine learning architecture is then constructed and extensive tests are performed to demonstrate how texture properties affect diagnostic performance. Thermal images are used to determine if they might be used to distinguish between radiographs of diseased and healthy knees. The outcomes of machine learning features and manually extracted features are compared. Finally, a stacked model that combines second-level machine learning with predictions of patellar texture and clinical characteristics is provided.
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