Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

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On Teacher-Student Semi-Supervised Learning for Chest X-ray Image Classification

10.21528/cbic2021-80 ◽

2021 ◽

Author(s):

Roberto Augusto Philippi Martins ◽

Danilo Silva

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Algorithm ◽

Unlabeled Data ◽

Imaging Data ◽

X Ray ◽

Teacher Student ◽

Chest X Ray ◽

Deep Learning Model

The lack of labeled data is one of the main prohibiting issues on the development of deep learning models, as they rely on large labeled datasets in order to achieve high accuracy in complex tasks. Our objective is to evaluate the performance gain of having additional unlabeled data in the training of a deep learning model when working with medical imaging data. We present a semi-supervised learning algorithm that utilizes a teacher-student paradigm in order to leverage unlabeled data in the classification of chest X-ray images. Using our algorithm on the ChestX-ray14 dataset, we manage to achieve a substantial increase in performance when using small labeled datasets. With our method, a model achieves an AUROC of 0.822 with only 2% labeled data and 0.865 with 5% labeled data, while a fully supervised method achieves an AUROC of 0.807 with 5% labeled data and only 0.845 with 10%.

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Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

Journal of Healthcare Engineering ◽

10.1155/2021/8829829 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Manjit Kaur ◽

Vijay Kumar ◽

Vaishali Yadav ◽

Dilbag Singh ◽

Naresh Kumar ◽

...

Keyword(s):

Deep Learning ◽

Learning Models ◽

Huge Number ◽

X Ray ◽

Screening Model ◽

Proposed Model ◽

Strength Pareto Evolutionary Algorithm ◽

Chest X Ray ◽

Deep Learning Model

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

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MANet: A Two-stage Deep Learning Method for Classification of COVID-19 from Chest X-ray Images

Neurocomputing ◽

10.1016/j.neucom.2021.03.034 ◽

2021 ◽

Author(s):

Yujia Xu ◽

Hak-Keung Lam ◽

Guangyu Jia

Keyword(s):

Deep Learning ◽

Learning Method ◽

Two Stage ◽

X Ray ◽

Chest X Ray

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COVID-19 detection in Chest X-ray Images using Deep Learning

10.21203/rs.3.rs-65954/v2 ◽

2020 ◽

Author(s):

Ebru Erdem ◽

Tolga Aydın

Keyword(s):

Deep Learning ◽

Deep Neural Networks ◽

Empirical Evaluation ◽

Specific Information ◽

The Novel ◽

X Ray ◽

Chest X Ray ◽

Lung Infections ◽

Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All the analyses were performed on a publicly available CXR dataset. This study offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of a disease. Empirical evaluation demonstrates that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.

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Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

Applied Sciences ◽

10.3390/app112210528 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10528

Author(s):

Khin Yadanar Win ◽

Noppadol Maneerat ◽

Syna Sreng ◽

Kazuhiko Hamamoto

Keyword(s):

Deep Learning ◽

Area Under Curve ◽

Ensemble Classifier ◽

Saliency Map ◽

X Rays ◽

Automated Classification ◽

Deep Convolutional Neural Networks ◽

X Ray ◽

Chest X Ray

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

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A deep learning method for classification of chest X-ray images

Journal of Physics Conference Series ◽

10.1088/1742-6596/1848/1/012030 ◽

2021 ◽

Vol 1848 (1) ◽

pp. 012030

Author(s):

Jiashi Zhao ◽

Mengmeng Li ◽

Weili Shi ◽

Yu Miao ◽

Zhengang Jiang ◽

...

Keyword(s):

Deep Learning ◽

Learning Method ◽

X Ray ◽

Chest X Ray

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Chest X-ray classification using Deep learning for automated COVID-19 screening

10.1101/2020.06.21.20136598 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ankita Shelke ◽

Madhura Inamdar ◽

Vruddhi Shah ◽

Amanshu Tiwari ◽

Aafiya Hussain ◽

...

Keyword(s):

Deep Learning ◽

Early Stage ◽

Ground Glass Opacity ◽

Classification Model ◽

X Rays ◽

X Ray ◽

The People ◽

History Of ◽

Chest X Ray

AbstractIn today’s world, we find ourselves struggling to fight one of the worst pandemics in the history of humanity known as COVID-2019 caused by a coronavirus. If we detect the virus at an early stage (before it enters the lower respiratory tract), the patient can be treated quickly. Once the virus reaches the lungs, we observe ground-glass opacity in the chest X-ray due to fibrosis in the lungs. Due to the significant differences between X-ray images of an infected and non-infected person, artificial intelligence techniques can be used to identify the presence and severity of the infection. We propose a classification model that can analyze the chest X-rays and help in the accurate diagnosis of COVID-19. Our methodology classifies the chest X-rays into 4 classes viz. normal, pneumonia, tuberculosis (TB), and COVID-19. Further, the X-rays indicating COVID-19 are classified on severity-basis into mild, medium, and severe. The deep learning model used for the classification of pneumonia, TB, and normal is VGG16 with an accuracy of 95.9 %. For the segregation of normal pneumonia and COVID-19, the DenseNet-161 was used with an accuracy of 98.9 %. ResNet-18 worked best for severity classification achieving accuracy up to 76 %. Our approach allows mass screening of the people using X-rays as a primary validation for COVID-19.

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COVID-19 detection in Chest X-ray Images using Deep Learning

10.21203/rs.3.rs-65954/v1 ◽

2020 ◽

Author(s):

Ebru Erdem ◽

Tolga Aydın

Keyword(s):

Deep Learning ◽

Deep Neural Networks ◽

Specific Information ◽

The Novel ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Lung Infections ◽

Day By Day

Abstract COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All analyzes were performed on CXR data received on the publicly available. This paper offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of disease. Empirical evaluations demonstrate that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models.

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