Abstract
Deep learning architectures have been extensively used in recent years for the classification of biomedical images to assist clinicians for diagnosis and treatment management of patients with different health conditions. These architectures have demonstrated expert level diagnosis, and in some cases, surpassed human experts in diagnosing health conditions. The automation tools based on deep learning frameworks have the potential to transform all stages of medical imaging pipeline from image acquisition to interpretation and analysis. One of the most common areas where these techniques are applied is knee MR image classification for different types of Anterior Cruciate Ligament (ACL) tears. If properly and timely managed, the diagnosis and treatment of ACL tear can avoid further degradation of patients’ knee joints and can also help slow the process of subsequent knee arthritis. In this work, we have implemented a novel classification framework based on multilayered basis pursuit algorithms inspired from recent research work in the area of the theoretical foundation of deep learning with the help of celebrated sparse coding theory. We implement an optimal multi-layered Convolutional Sparse Coding (ML-CSC) framework for classification of a labelled dataset of knee MR images with the coronal view and compare the results with traditional convolutional neural network (CNN) based classifiers. Empirical results demonstrate the effectiveness of the ML-CSC framework and show that the framework can successfully learn distinct features on a small dataset and achieve a good efficiency of more than 92% without employing regularization techniques and extensive training on large datasets. In addition to 95% average accuracy on the presence and absence of ACL tears, the framework also performs well on the imbalanced and challenging classification of partial ACL tear with 85% accuracy.
Highlights
One of the most common sports injuries in young adults is anterior cruciate ligament (ACL) tear
In this work we address the above-mentioned gaps with the implementation of a Deep learning (DL) classification framework optimally designed and tuned for gray scale magnetic resonance (MR) images obtained at Hospital Kuala Lumpur (HKL) and labeled by expert radiologist for normal, complete and partial ACL tears
The images were labeled by certified MSK-radiologist at Hospital Kuala Lumpur (HKL) and have been used in [39], for classification of MR images with convolutional neural network (CNN)
Summary
One of the most common sports injuries in young adults is anterior cruciate ligament (ACL) tear. The primary advantage of learning representations through deep neural networks is their ability to learn semantically meaningful patterns and features in underlying data without explicit human intervention These models once trained successfully on training datasets, can be effectively used for solution of range of problems like image recognition and image classification on (unseen) test data. The solution to challenging partial ACL tear classification problem, where the classifiers generally do not give good accuracies, is optimized with data augmentation techniques and accuracy of more than 85% on this specific class is achieved for multilayer iterative thresholding algorithm (ML-ISTA) framework, outperforming traditional CNN with same number of parameters. The accuracies of all models are compared with CNN, demonstrating the viability and effectiveness of the MR image classification framework
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