Knee Osteoarthritis Detection & Severity Prediction

Abstract:

Arthritis is a condition that can affect one or more joints, and symptoms might include swelling, soreness, inflammation, stiffness, and so on. Arthritis is more prevalent in elderly persons, and its symptoms often get more severe with increasing age. The most common kind of arthritis is osteoarthritis, despite the fact that there are a number of other varieties of arthritis, each with its own set of causes and therapies.

It is estimated that over 237 million individuals throughout the world suffer with osteoarthritis; this is approximately 3.3% of the total human population. The importance of arthritis screening at an early stage cannot be overstated, despite the fact that there is no known treatment for the condition at this time.

Patients can get assistance diagnosing osteoarthritis in their knees by using a tool called the Knee Osteoarthritis Detection. This web tool not only assists with the early discovery of the condition, but it also determines the degree to which the disorder is present. The patient has the choice to register and provide his information at any time during the process.

He is required to provide his name, username, an x-ray photo, a photo of his guardian, his own photo, an identification photo, and a photo of his license, in addition to other information. There can be persons here with the same name, but there can’t be two users with the same username. Because of this, there won’t be any problems with the match. Every individual will be given a distinct diagnosis, complete with symptoms and a treatment plan, as well as a one-of-a-kind username.

In the beginning, we train the datasets. We have five distinct classes of pictures, and after training using the CNN technique that is being utilized in this project, we have various convolutional layers developing on it. After each layer, the precision continues increasing, so we have five different classes.

The amount of eposch that are utilized determines the number of layers that are utilized. The greater the total number of eposch, the more accurate the results. Once a.h5 file has been prepared, it will contain all of the properties as well as the confidence score for each detection class.

After the x-ray photo has been uploaded, it is resized, its color is changed to gray, and then several convolutional layers are applied to it with the assistance of the CNN algorithm being used in this project. Finally, a confidence score is generated, and it is matched with the confidence score in the.h5 file, at which point the class is detected, and detection is therefore completed.

Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.