Anterior cruciate ligament tear badminton player diagnosis using a pipeline technique using courtship water strider algorithm

The ACL (Anterior Cruciate Ligament) tear has been known as a frequent damage amid sportsmen. This injury occurs when the ACL, a main ligament in the knee, rips, leading to knee instability. The knee is a frequently damaged joint in the body. Clinical testing alone is not a valid technique for assessing meniscal and ligament lesions of the knee. Magnetic resonance imaging (MRI) could considerably boost the diagnostic accuracy of numerous lesions in the knee, and its non-invasiveness has been counted as an advantage over arthroscopy in the event of traumatic knee lesions. This research presents an automated technique to attain suitable accuracy and better speed in detecting ACL damage. The current work presents a novel pipeline approach for the automated diagnosis of ACL injury using MRI. Feature extraction is undertaken on the images of input, and then preprocessing is done to decrease the complexity of the system. The characteristics are then incorporated into an optimum artificial neural network to produce an effective diagnostic method. Categorization and selection of features have been done using an upgraded meta-heuristic, termed the courtship water strider algorithm. Then, the suggested approach has been applied to the database of “DICOM MRIs”; in addition, the outputs are verified by comparison with several previously published methods. Simulation findings reveal the increased efficiency of the recommended technique when compared with the other studied processes.