Affiliation:
1. Department of Automation, Xiamen University, Xiamen 361102, China
Abstract
The anterior cruciate ligament (ACL) is critical for controlling the motion of the knee joint, but it is prone to injury during sports activities and physical work. If left untreated, ACL injuries can lead to various pathologies such as meniscal damage and osteoarthritis. While previous studies have used deep learning to diagnose ACL tears, there has been a lack of standardization in human unit classification, leading to mismatches between their findings and actual clinical diagnoses. To address this, we perform a triple classification task based on various tear classes using an ordinal loss on the KneeMRI dataset. We utilize a channel correction module to address image distribution issues across multiple patients, along with a spatial attention module, and test its effectiveness with various backbone networks. Our results show that the modules are effective on various backbone networks, achieving an accuracy of 83.3% on ResNet-18, a 6.65% improvement compared to the baseline. Additionally, we carry out an ablation experiment to verify the effectiveness of the three modules and present our findings with figures and tables. Overall, our study demonstrates the potential of deep learning in diagnosing ACL tear and provides insights into improving the accuracy and standardization of such diagnoses.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
1 articles.
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1. Anterior Cruciate Ligament Injury Classification from MRI Scans Using Deep Learning;2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT);2023-12-14