Detection of Anterior Cruciate Ligament Tear using CNN for Diagnostic Approach

The detection of Anterior Cruciate Ligament tears is hard for the timely and accurate diagnosis and treatment of knee injuries. Traditional methodologies for ACL tear detection using MRI images face challenges such as class imbalance, computational complexity, limited accuracy. This study inspects how Convolution Neural Network (CNNs) and augmentation techniques is applied for the detection of ACL tears from MRI images which helps in robust preprocessing and enhanced classification accuracy. The data set of knee MRIs explains the presence or absence of ACL tears which was helped to train and evaluate CNN model. The approach which is proposed involves preprocessing of the images uses normalization and augmentation techniques which helps in improving the model’s robustness. A CNN architecture influenced by transfer learning and was included to take out relevant features and to classify the images By using CNN the results were improved with high accuracy and reliability in detecting ACL tears, showcasing its strength in assisting radiologists in their clinics. The utilization of the trained model in a clinical workflow can reduce the workload, diagnostic time for radiologists and making sure of accurate patient care.