Multi-layered basis pursuit algorithms for classification of MR images of Knee ACL tear

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.