A Review of an Early Detection and Quantification of Osteoarthritis Severity in Knee using Machine Learning Techniques. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Srinivasulu Raju, S; Niranjan, T; Pandiyan, P; Sai Snehitha, M
• Is Part Of: IOP conference series. Volume 1057:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1057/1/012095
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The major cause of frailty in older and overweight people is Osteoarthritis. It is a joint illness that generally influences the ligament that could be principally affects the cartilage. Cartilage is a smooth elastic tissue which makes the bones move easily, stabilizes the joint and prevents them from abrading one another. The protective cartilage in Osteoarthritis is ruptured and makes the bones rub one another, inflicting joint stiffness and excessive pain. The current system for the assessment of Osteoarthritis incorporates clinical examination and restorative imaging methods. In this project, by utilizing profound features and medical images we need to recognize and classify OA affected in knee. This can impact on the detection and classification of target area in images and cause of it the irrelevant features could be selected from the medical images. This project will moreover center on dealing the colossal quantity of image information by using high speed computing. This paper also discovers the Magnetic Resonance imaging (MRI) techniques for detection and classification of Osteoarthritis in descriptive and comparative manner. Hence a coordinating discourse of different location strategies, highlight extraction procedures and classification methods regarding Osteoarthritis is worn out in a logical way.