Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

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Abstract

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks ( CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose ( DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin, Holger R. Roth, Mingchen Gao … (2016)

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

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Automatic tuberculosis screening using chest radiographs.

George Thoma, Rahul Singh, Kannappan Palaniappan … (2014)

Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.

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Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.

Zhiyun Xue, Rahul Singh, George Thoma … (2014)

The National Library of Medicine (NLM) is developing a digital chest X-ray (CXR) screening system for deployment in resource constrained communities and developing countries worldwide with a focus on early detection of tuberculosis. A critical component in the computer-aided diagnosis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance. The method consists of three main stages: 1) a content-based image retrieval approach for identifying training images (with masks) most similar to the patient CXR using a partial Radon transform and Bhattacharyya shape similarity measure, 2) creating the initial patient-specific anatomical model of lung shape using SIFT-flow for deformable registration of training masks to the patient CXR, and 3) extracting refined lung boundaries using a graph cuts optimization approach with a customized energy function. Our average accuracy of 95.4% on the public JSRT database is the highest among published results. A similar degree of accuracy of 94.1% and 91.7% on two new CXR datasets from Montgomery County, MD, USA, and India, respectively, demonstrates the robustness of our lung segmentation approach.

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Contributors

Asmaa Abbas: asmaa.abbas@science.aun.edu.eg

Bio :

Asmaa Abbas

was born in Assiut city, Egypt. She finished her B.Sc. in Computer Science from the Department of Mathematics at Assiut University in 2004. Asmaa has got MSc in computer science from the Mathematics Department at the Assiut University in 2020. She is currently a researcher at Mathematics Department in Assiut University. Her research interests include deep learning, medical image analysis, and data mining.

Mohammed M. Abdelsamea: mohammed.abdelsamea@bcu.ac.uk

Bio :

Mohammed M. Abdelsamea

is currently a Senior Lecturer in Data and Information Science at the School of Compu- ting and Digital Technology, Birmingham City University. Before joining BCU, he worked for the School of Computer Science at Nottingham Univer- sity, Mechanochemical Cell Biology at Warwick University, Nottingham Molecular Pathology Node (NMPN), and Division of Cancer and Stem Cells both at Nottingham Medical School. In 2016, he was a Marie Curie Research Fellow at the School of Computer Science at Nottingham University. Before moving to the UK, he worked as an Assistant Professor of Computer Science for Assiut University, Egypt. He was also a Visiting Researcher at Robert Gordon University, Aberdeen, UK. Abdelsamea has received his Ph.D. degree (with Doctor Europaeus) in Computer Science and Engineering from IMT - Institute for Advanced Studies, Lucca, Italy. His main research interests are in computer vision including image processing, deep learning, data mining and machine learning, pattern recognition, and image analysis.

Mohamed Medhat Gaber: mohamed.gaber@bcu.ac.uk

Bio :

Mohamed Medhat Gaber

is a Professor in Data Analytics at the School of Computing and Digital Technology, Birmingham City University.Mohamed received his PhD from Monash University, Australia. He then held appointments with the University of Sydney, CSIRO, and Monash University, all in Australia. Prior to joining Birmingham City University, Mohamed worked for the Robert Gordon University as a Reader in Computer Science and at the University of Portsmouth as a Senior Lecturer in Computer Science, both in the UK. He has published over 200 papers, coauthored 3 monograph-style books, and edited/co-edited6 books on data mining and knowledge discovery. His work has attracted well over four thousand citations, with an h-index of 35. Mohamed has served in the program committees of major conferences related to data mining, including ICDM, PAKDD, ECML/PKDD and ICML. He has also co-chaired numerous scientific events on various data mining topics. Professor Gaber is recognised as a Fellow of the British Higher Education Academy (HEA). He is also a member of the International Panel of Expert Advisers for the Australasian Data Mining Conferences. In 2007, he was awarded the CSIRO teamwork award.

Journal

Journal ID (iso-abbrev): Appl Intell

Title: Applied Intelligence

Publisher: Springer US (New York )

ISSN (Print): 0924-669X

ISSN (Electronic): 1573-7497

Publication date (Electronic): 5 September 2020

Publication date PMC-release: 5 September 2020

Pages: 1-11

Affiliations

[1 ]GRID grid.252487.e, ISNI 0000 0000 8632 679X, Mathematics Department, , Faculty of Science, Assiut University, ; Assiut, Egypt

[2 ]GRID grid.19822.30, ISNI 0000 0001 2180 2449, School of Computing and Digital Technology, , Birmingham City University, ; Birmingham, UK

Article

Publisher ID: 1829

DOI: 10.1007/s10489-020-01829-7

PMC ID: 7474514

PubMed ID: 34764548

SO-VID: fe8dda97-ef47-489c-a0e9-9518fab21d98

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Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

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Novel Coronavirus Disease COVID-19

Most cited references 8

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Automatic tuberculosis screening using chest radiographs.

Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration.

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