Identification and Categorization of Knee Osteoarthritis Severity Using Deep Learning Techniques

Abstract

In biomedical engineering, one of the critical challenges lies in the accurate newlinedetection of degenerative conditions, such as knee osteoarthritis (OA). Traditionally, the newlineevaluation of OA severity is performed through manual grading techniques, which are not newlineonly time-intensive but also susceptible to variability between observers. This variability newlinecan delay diagnosis and hinder early intervention. Consequently, there has been a growing newlineinterest in Computer-Aided Diagnosis (CAD) systems, driven by the need for more newlineefficient and reliable methods of identifying OA. This research aims to develop a CAD newlineframework specifically designed to automate the detection and classification of knee OA newlineseverity, utilizing deep learning approaches. By improving the accuracy and speed of newlinediagnosis, this system can facilitate better patient outcomes and enhance the overall newlinemanagement of knee osteoarthritis. newlineKnee Osteoarthritis (KOA) is one of the leading causes of disability newlineworldwide, especially affecting the elderly population. Early diagnosis and accurate newlineclassification of KOA severity are crucial for effective treatment and management. In this newlineresearch, we propose an innovative approach that leverages advanced computer vision newlineand machine learning techniques to enhance the detection and classification of KOA newlineseverity stages. The study is divided into two key phases: knee joint detection and severity newlineclassification newline