CLASSIFICATION OF MAGNETIC RESONANCE IMAGES USING K-NEIGHBOUR ALGORITHM

Abstract

An MRI test uses magnets and radio waves to capture images inside your body without making a surgical incision. It can be performed on any part of your body. A knee MRI looks specifically at your knee and its surrounding areas. An MRI lets your doctor see the soft tissues in your body along with the bones. This allows them to inspect the elements of the knee that might have been injured during physical activity or from wear and tear. The test can also provide detailed images of various sections of the knee, such as bones, cartilage, tendons, muscles, blood vessels, and ligaments. An MRI takes images in better contrast than other tests. Your doctor may want you to undergo a special kind of MRI called an MRI arteriogram. For this procedure, your doctor will inject a contrast fluid, or dye, into your knee to provide a better view of its structure. The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It's easy to implement and understand, but has a major drawback of becoming significantly slower as the size of that data in use grows.