Anterior cruciate ligament tear detection based on deep belief networks and improved honey badger algorithm

The Anterior Cruciate Ligament (ACL) tear is a common injury among athletes who participate in extreme sports such as basketball, football, American football, and skiing. When an ACL tear is suspected, doctors usually take XRays of the patient's knee to identify the injury. MRI can often be used to help with diagnosis. This study proposes a novel hierarchical approach for more accurate ACL injury detection. The method starts by applying preprocessing techniques to improve image quality, then using Co-occurrence Matrix (GLCM) and Discrete Cosine Transform (DCT) in combination, and features from the images are retrieved. The features are then sent into a Deep Belief Network (DBN) which has been trained for classification and is further optimized using a new metaheuristic method known as the "Improved Honey Badger Algorithm". Results are compared with methods like Euclidean Distance and Neural Networks (ED/NN), Random Forest (RF), Fuzzy and Convolutional Neural Networks (CNN) and it is seen that the proposed method achieves 96% accuracy, 98% sensitivity, and 80% specificity, proving highest efficiency than all other methods.