Joshi Kavita, Suganthi K
Vellore Institute of Technology, Chennai 600127, India.
Diagnostics (Basel). 2022 Sep 26;12(10):2314. doi: 10.3390/diagnostics12102314.
Anterior cruciate ligament (ACL) tear is very common in football players, volleyball players, sprinters, runners, etc. It occurs frequently due to extra stretching and sudden movement and causes extreme pain to the patient. Various computer vision-based techniques have been employed for ACL tear detection, but the performance of most of these systems is challenging because of the complex structure of knee ligaments. This paper presents a three-layered compact parallel deep convolutional neural network (CPDCNN) to enhance the feature distinctiveness of the knee MRI images for anterior cruciate ligament (ACL) tear detection in knee MRI images. The performance of the proposed approach is evaluated for the MRNet knee images dataset using accuracy, recall, precision, and the F1 score. The proposed CPDCNN offers an overall accuracy of 96.60%, a recall rate of 0.9668, a precision of 0.9654, and an F1 score of 0.9582, which shows superiority over the existing state-of-the-art methods for knee tear detection.
前交叉韧带（ACL）撕裂在足球运动员、排球运动员、短跑运动员、跑步者等人群中非常常见。它经常由于过度拉伸和突然运动而发生，给患者带来极度疼痛。各种基于计算机视觉的技术已被用于ACL撕裂检测，但由于膝关节韧带结构复杂，这些系统中的大多数性能都具有挑战性。本文提出了一种三层紧凑并行深度卷积神经网络（CPDCNN），以增强膝关节MRI图像的特征独特性，用于膝关节MRI图像中的前交叉韧带（ACL）撕裂检测。使用准确率、召回率、精确率和F1分数对所提出方法在MRNet膝关节图像数据集上的性能进行了评估。所提出的CPDCNN的总体准确率为96.60%，召回率为0.9668，精确率为0.9654，F1分数为0.9582，这表明其在现有最先进的膝关节撕裂检测方法中具有优越性。
