Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare

Accurate and automated assessment of anterior cruciate ligament (ACL) injuries in MR images is essential for athlete healthcare and rapid diagnosis of knee injuries. However, challenges such as the small size of the ligament, variations in MR image quality, and complex anatomical structures complicate the classification process.

In this study, we propose a hierarchical deep learning model for the detection and classification of ACL injuries. The model consists of two main phases: ACL segmentation and injury classification. In the first phase, we employ an encoder-decoder architecture with attention mechanisms to accurately identify the ACL region in MR images, while suppressing background noise. Skip connections are used to preserve spatial details and improve segmentation accuracy. In the second phase, the segmented ACL region is input into a hierarchical convolutional neural network (CNN) for classification. Dense blocks are incorporated to maximize feature reuse, while max-pooling and global average pooling (GAP) layers help to reduce overfitting and improve feature extraction.

The proposed method was evaluated on a knee MRI dataset and compared with other state-of-the-art approaches. Our model demonstrated high accuracy in both segmentation and classification tasks, owing to the integration of attention mechanisms and hierarchical feature extraction.

This approach offers a robust solution for the automated assessment of ACL injuries, providing clinicians and sports medicine specialists with a reliable tool for more efficient and accurate diagnosis.