Multi-Layered Basis Pursuit Algorithms for Classification of MR Images of Knee ACL Tear

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...

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Veröffentlicht in:	IEEE access Jg. 8; S. 205424 - 205435
Hauptverfasser:	Wahid, Abdul, Shah, Jawad Ali, Khan, Adnan Umar, Ullah, Mukhtar, Ayob, Mohd Zaki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Arthritis Artificial neural networks Basis pursuit Classification Classification algorithms Coding Datasets Deep learning Diagnosis Dictionaries Empirical analysis Image acquisition Image classification Injuries Iterative algorithms iterative shrinkage algorithms Knee knee MR image classification Machine learning Medical diagnosis Medical imaging multi-layer convolutional sparse coding Multilayers Pursuit algorithms Regularization Surgical implants Task analysis
ISSN:	2169-3536, 2169-3536
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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.
AbstractList	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.
Author	Shah, Jawad Ali Ayob, Mohd Zaki Khan, Adnan Umar Wahid, Abdul Ullah, Mukhtar
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References	ref34 ref12 rajpurkar (ref19) 2017 ref15 ref36 luo (ref13) 2018; 29 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref1 makhmalbaf (ref26) 2013; 1 ref39 ref18 krizhevsky (ref16) 2012 gregor (ref35) 2010 ref24 ref23 ref25 ref20 ref22 ref21 ref28 papyan (ref14) 2017; 18 ref27 ref29 ref8 chen (ref37) 2018 ref7 ref9 ref4 ref3 ref6 ref5 ablin (ref38) 2019 rajpurkar (ref17) 2017
References_xml	– ident: ref28 doi: 10.1137/1.9781611974997 – ident: ref1 doi: 10.1177/0363546516629944 – ident: ref2 doi: 10.1002/jor.23632 – ident: ref12 doi: 10.1109/TPAMI.2008.79 – start-page: 13100 year: 2019 ident: ref38 article-title: Learning step sizes for unfolded sparse coding publication-title: Proc Adv Neural Inf Process Syst – ident: ref25 doi: 10.1109/TPAMI.2019.2904255 – ident: ref20 doi: 10.1148/ryai.2019180091 – ident: ref22 doi: 10.1002/jor.23861 – volume: 18 start-page: 2887 year: 2017 ident: ref14 article-title: Convolutional neural networks analyzed via convolutional sparse coding publication-title: J Mach Learn Res – ident: ref29 doi: 10.1109/CVPR.2018.00196 – ident: ref8 doi: 10.1109/TMI.2017.2760978 – volume: 1 start-page: 94 year: 2013 ident: ref26 article-title: Accuracy of Lachman and anterior drawer tests for anterior cruciate ligament injuries publication-title: Arch Bone Jt Surg – ident: ref7 doi: 10.1109/MSP.2017.2739299 – ident: ref32 doi: 10.1137/080716542 – year: 2017 ident: ref19 article-title: Cardiologist-level arrhythmia detection with convolutional neural networks publication-title: arXiv 1707 01836 – ident: ref36 doi: 10.1109/TPAMI.2015.2392779 – ident: ref3 doi: 10.1016/j.knee.2008.11.004 – ident: ref21 doi: 10.1007/s10278-019-00193-4 – ident: ref10 doi: 10.1109/CVPR.2019.00218 – ident: ref15 doi: 10.1109/MSP.2018.2820224 – volume: 29 start-page: 3289 year: 2018 ident: ref13 article-title: Convolutional sparse autoencoders for image classification publication-title: IEEE Trans Neural Netw Learn Syst – ident: ref6 doi: 10.1109/TNNLS.2018.2876865 – ident: ref11 doi: 10.1007/978-1-4419-7011-4 – start-page: 399 year: 2010 ident: ref35 article-title: Learning fast approximations of sparse coding publication-title: Proc 27th Int Conf Int Conf Mach Learn – ident: ref33 doi: 10.1214/11-AOS878 – ident: ref18 doi: 10.1109/CVPR.2017.369 – ident: ref4 doi: 10.1302/0301-620X.68B3.3755441 – ident: ref23 doi: 10.1109/ACCESS.2018.2863606 – ident: ref39 doi: 10.1145/3362752.3365196 – ident: ref27 doi: 10.1038/s41568-018-0016-5 – start-page: 1097 year: 2012 ident: ref16 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref24 doi: 10.1137/18M1183352 – start-page: 9061 year: 2018 ident: ref37 article-title: Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds publication-title: Proc Adv Neural Inf Process Syst – year: 2017 ident: ref17 article-title: CheXNet: Radiologist-level pneumonia detection on chest X-Rays with deep learning publication-title: arXiv 1711 05225 – ident: ref9 doi: 10.1109/BIBE.2019.00182 – ident: ref5 doi: 10.1148/radiology.167.3.3363138 – ident: ref34 doi: 10.1109/ICASSP.2008.4518498 – ident: ref31 doi: 10.1002/cpa.20042 – ident: ref30 doi: 10.1007/978-3-030-01267-0_50
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SubjectTerms	Algorithms Arthritis Artificial neural networks Basis pursuit Classification Classification algorithms Coding Datasets Deep learning Diagnosis Dictionaries Empirical analysis Image acquisition Image classification Injuries Iterative algorithms iterative shrinkage algorithms Knee knee MR image classification Machine learning Medical diagnosis Medical imaging multi-layer convolutional sparse coding Multilayers Pursuit algorithms Regularization Surgical implants Task analysis
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Title	Multi-Layered Basis Pursuit Algorithms for Classification of MR Images of Knee ACL Tear
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