A DEVELOPMENT OF ANTERIOR CRUCIATE LIGAMENT (ACL) DIAGNOSIS SYSTEM USING FUZZY INFERENCE SYSTEM

Abstract

Knee injury is one of the most common injuries in sports activities or events. Failure to detect it would jeopardize the athletes’ future. Knee image processing is studied for the development of an aided system to identify knee injury. However, medical experts analyze the MRI images using their naked eyes. This increases the possibilities for false analysis. To overcome the problem, this study aims to develop an intelligent system which involves a Fuzzy Inference System (FIS) to assist the medical experts in making decisions to decide on the types of ACL knee injury. The end results in the identification of ACL injury is in the form of a classification based on complete tear (CT), a partial tear (PT), and normal classes. The analysis of results based on comparison between the FIS and medical experts’ opinion reveals an accuracy of 92%.