<![CDATA[This research explores the application of Generative Adversarial Networks (GANs) for detecting and classifying Anterior Cruciate Ligament (ACL) injuries using MRI images. The study utilized a dataset of 917 MRI images, each labeled as healthy, partially injured, or completely ruptured, to train the model. The performance of the GAN model was evaluated using a confusion matrix and a classification report, yielding an overall accuracy of 92%. The model demonstrated high proficiency in identifying healthy ACLs and partially injured ACLs but encountered some challenges in accurately identifying completely ruptured ACLs. Despite this, the results suggest that machine learning techniques, particularly GANs, have significant potential for enhancing the accuracy and efficiency of ACL injury detection. The ability of the model to distinguish between different degrees of injury could potentially aid in treatment planning. However, the study also underscores the need for further refinement of the model, particularly in improving its sensitivity in detecting severe ACL injuries. This research highlights the potential of machine learning in medical imaging and provides a solid foundation for future research in ACL injury detection and classification.]]>
Copyrights © 2024
