Abstract
Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical assessments and MRI, have limitations in terms of accuracy and efficiency. This study introduces a new diagnostic approach by combining of the deep learning architecture Res2Net with an improved version of the Lévy flight distribution (ILFD) to improve the detection of ACL tears in knee MRI images. The Res2Net model is known for its ability to extract important features and classify them effectively. By optimizing the model using the ILFD algorithm, the diagnostic efficiency is greatly improved. For validation of the proposed model's efficiency, it has been applied into two standard datasets including Stanford University Medical Center and Clinical Hospital Centre Rijeka. Comparative analysis with existing diagnostic methods, including 14 layers ResNet-14, Compact Parallel Deep Convolutional Neural Network (CPDCNN), Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and combined CNN and Modified Golden Search Algorithm (CNN/MGSA) shows that the suggested Res2Net/ILFD model performs better in various metrics, including precision, recall, accuracy, f1-score, and specificity, and Matthews correlation coefficient.