Prediction of Knee Osteoarthritis Using Deep Learning
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Abstract
Knee osteoarthritis (OA) is a disease that increases in incidence and prevalence with advancing age, resulting in symptomatic knee OA in those over the age of 60, around 10 per cent of men and 13 per cent of women. Knee osteoarthritis (OA) is a chronic degenerative joint disease characterized by cartilage loss and changes in bones underneath it, causing pain and functional disability. The main clinical symptoms of knee. OA are pain and stiffness, particularly after activity, leading to reduced mobility and quality of life, and eventually resulting in knee replacement surgery. OA is one of the leading causes of global disability in people aged 65 and older, and its burden is likely to increase in the future with the ageing of the population and rise in obesity worldwide. OA is mainly diagnosed in clinical studies by means of medical images. X-ray imaging creates pictures of the inside of your body. The images show the parts of your body in different shades of black and white. This is because different tissues absorb different amounts of radiation. Calcium in bones absorbs x-rays the most, so bones look white. The typical symptoms of KOA include pain, stiffness, decreased joint range of motion, and gait dysfunctions, which worsen in accordance with an increase in the disease progression. OA is mainly diagnosed through medical images. It can be predicted using x-ray or mri images. The primary goal of this project was to develop an automated classification model fr Knee Osteoarthritis, based on the Kellgren-Lawrence(KL) grading system, using radiographic imaging and obtain satisfactory results for further diagnosis.
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