Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can...

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Vydané v:	Biomedical engineering online Ročník 17; číslo 1; s. 63 - 19
Hlavní autori:	Wang, Changmiao, Elazab, Ahmed, Jia, Fucang, Wu, Jianhuang, Hu, Qingmao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 23.05.2018 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Accuracy Advances in Medical Robotics and Automation for Surgery and Rehabilitation Artificial intelligence Autoencoder Automation Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Care and treatment Chest Chest screening Chest x-rays Classification Computer aided diagnosis Datasets Deep learning Diagnosis Engineering Health screening Humans Image Processing, Computer-Assisted - methods Information processing International conferences Lesions Lung - diagnostic imaging Lung diseases Machine Learning Medical imaging Methods Neural networks Noise reduction Pattern recognition Radiography, Thoracic Radiology Receiver operating characteristic Reconstruction Regulatory approval ROC Curve Screening Signal-To-Noise Ratio Transfer learning Triage - methods X-rays China Deep learning Computer aided diagnosis Receiver operating characteristic Autoencoder Chest screening
ISSN:	1475-925X, 1475-925X
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Abstract	Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist’s workload on scrutinizing the medical images. Method We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. Results We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. Conclusion The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.
AbstractList	Abstract Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist’s workload on scrutinizing the medical images. Method We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. Results We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. Conclusion The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist’s workload on scrutinizing the medical images. Method We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. Results We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. Conclusion The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images. We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist’s workload on scrutinizing the medical images. Method We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. Results We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. Conclusion The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images. We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images. Method We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. Results We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. Conclusion The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs. Keywords: Chest screening, Computer aided diagnosis, Deep learning, Autoencoder, Receiver operating characteristic In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images.OBJECTIVEIn this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images.We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into.METHODWe present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into.We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively.RESULTSWe only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively.The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.CONCLUSIONThe results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.
ArticleNumber	63
Audience	Academic
Author	Wang, Changmiao Jia, Fucang Elazab, Ahmed Wu, Jianhuang Hu, Qingmao
Author_xml	– sequence: 1 givenname: Changmiao surname: Wang fullname: Wang, Changmiao organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences – sequence: 2 givenname: Ahmed surname: Elazab fullname: Elazab, Ahmed organization: Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Department of Computer Science, Misr Higher Institute for Commerce and Computers – sequence: 3 givenname: Fucang surname: Jia fullname: Jia, Fucang organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences – sequence: 4 givenname: Jianhuang surname: Wu fullname: Wu, Jianhuang organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences – sequence: 5 givenname: Qingmao surname: Hu fullname: Hu, Qingmao email: qm.hu@siat.ac.cn organization: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Key Laboratory of Human-Machine Intelligence Synergy Systems
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29792208$$D View this record in MEDLINE/PubMed
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Keywords	Deep learning Computer aided diagnosis Receiver operating characteristic Autoencoder Chest screening
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Snippet	Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions... In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of... Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions... Abstract Objective In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions...
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SubjectTerms	Accuracy Advances in Medical Robotics and Automation for Surgery and Rehabilitation Artificial intelligence Autoencoder Automation Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Care and treatment Chest Chest screening Chest x-rays Classification Computer aided diagnosis Datasets Deep learning Diagnosis Engineering Health screening Humans Image Processing, Computer-Assisted - methods Information processing International conferences Lesions Lung - diagnostic imaging Lung diseases Machine Learning Medical imaging Methods Neural networks Noise reduction Pattern recognition Radiography, Thoracic Radiology Receiver operating characteristic Reconstruction Regulatory approval ROC Curve Screening Signal-To-Noise Ratio Transfer learning Triage - methods X-rays
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Title	Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
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