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

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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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences ◽

10.3390/app112110301 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10301

Author(s):

Muhammad Shoaib Farooq ◽

Attique Ur Rehman ◽

Muhammad Idrees ◽

Muhammad Ahsan Raza ◽

Jehad Ali ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real World ◽

Early Stage ◽

Binary Classification ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

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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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Accessory pathway analysis using a multimodal deep learning model

Scientific Reports ◽

10.1038/s41598-021-87631-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Makoto Nishimori ◽

Kunihiko Kiuchi ◽

Kunihiro Nishimura ◽

Kengo Kusano ◽

Akihiro Yoshida ◽

...

Keyword(s):

Deep Learning ◽

Electrophysiological Study ◽

Accessory Pathway ◽

Learning Model ◽

X Rays ◽

X Ray ◽

Wpw Syndrome ◽

Chest X Ray ◽

Deep Learning Model ◽

Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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Deep Learning Classification of Chest X-Ray Images

2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) ◽

10.1109/ssiai49293.2020.9094612 ◽

2020 ◽

Author(s):

Mohammad S. Majdi ◽

Khalil N. Salman ◽

Michael F. Morris ◽

Nirav C. Merchant ◽

Jeffrey J. Rodriguez

Keyword(s):

Deep Learning ◽

X Ray ◽

Chest X Ray

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Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17186933 ◽

2020 ◽

Vol 17 (18) ◽

pp. 6933 ◽

Cited By ~ 7

Author(s):

Enzo Tartaglione ◽

Carlo Alberto Barbano ◽

Claudio Berzovini ◽

Marco Calandri ◽

Marco Grangetto

Keyword(s):

Deep Learning ◽

Scientific Community ◽

Northern Italy ◽

Medium Size ◽

Small Data ◽

Early Screening ◽

X Ray ◽

Chest X Ray ◽

Statistical Results

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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Molluscum-Like Papules as a Presentation of Early Papulonecrotic Tuberculid in Association with Nodular Tuberculid in a Male with Asymptomatic Active Pulmonary Tuberculosis

Journal of Cutaneous Medicine and Surgery ◽

10.2310/7750.2014.14035 ◽

2015 ◽

Vol 19 (2) ◽

pp. 159-162 ◽

Cited By ~ 3

Author(s):

Rachel Asiniwasis ◽

Maha T. Dutil ◽

Scott Walsh

Keyword(s):

Pulmonary Tuberculosis ◽

Early Stage ◽

Induced Sputum ◽

Simultaneous Occurrence ◽

Antituberculous Therapy ◽

X Ray ◽

Active Pulmonary Tuberculosis ◽

History Of ◽

Chest X Ray ◽

Histopathologic Findings

Background/Objectives The clinical and histopathologic findings of a rare simultaneous occurrence of papulonecrotic tuberculid and nodular tuberclid in a patient with active but asymptomatic pulmonary tuberculosis are presented. Papulonecrotic tuberculid was observed at a very early stage, presenting as molluscum-like lesions. This has been described once in the literature. This was observed in conjunction with lesions compatible with the rare clinicopathologic variant of nodular tuberculid. Critical to the diagnosis of active pulmonary tuberculosis was the use of induced sputum testing, which confirmed the diagnosis despite the lack of a cough and a chest x-ray negative for active tuberculosis. Methods/Results A 40-year-old male presented with a 2-week history of fever and a skin eruption consisting of molluscum-like papules on the ears, arms, and abdomen and nodules on his legs. Biopsies from both lesions were consistent with papulonecrotic and nodular tuberculid, respectively. Despite the lack of any respiratory symptoms, induced sputum grew Mycobacterium tuberculosis, and the lesions resolved on antituberculous therapy. Conclusions and Relevance Tuberculids are rare in Western countries but must be considered in the differential diagnosis of eruptions in patients from endemic countries. An active tuberculous focus must be sought out.

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Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽

10.3390/covid1010034 ◽

2021 ◽

Vol 1 (1) ◽

pp. 403-415

Author(s):

Abeer Badawi ◽

Khalid Elgazzar

Keyword(s):

Deep Learning ◽

Binary Classification ◽

Healthcare Providers ◽

Large Set ◽

X Rays ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Multi Class Classification ◽

Novel Coronavirus

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

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COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

10.21203/rs.3.rs-34534/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tahmina Zebin ◽

Shahadate Rezvy ◽

Wei Pang

Keyword(s):

Disease Progression ◽

Detection Accuracy ◽

X Rays ◽

Automated Classification ◽

X Ray ◽

Feature Extractor ◽

Chest X Ray ◽

Activation Mapping ◽

Activation Map

Abstract Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We applied and implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets {https://github.com/ieee8023/covid-chestxray-dataset},{https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia}}. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and pneumonia (viral and bacterial) from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 91.2% , 95.3%, 96.7% for the VGG16, ResNet50 and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a cycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we visualized the regions of input that are important for predictions and a gradient class activation mapping (Grad-CAM) technique is used in the pipeline to produce a coarse localization map of the highlighted regions in the image. This activation map can be used to monitor affected lung regions during disease progression and severity stages.

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Unilateral Right Exophthalmia Revealing Systemic Sarcoidosis: A Case Report and a Review of the Literature

EMJ Respiratory ◽

10.33590/emjresp/19-00088 ◽

2019 ◽

Author(s):

Sophia Bania

Keyword(s):

Ct Scan ◽

Exceptional Case ◽

Mediastinal Lymphadenopathy ◽

Case Presentation ◽

X Ray ◽

Caseous Necrosis ◽

History Of ◽

Chest X Ray ◽

Systemic Sarcoidosis

Background: Sarcoidosis is only revealed in 3% of the cases among Caucasians by ophthalmic damage and, when it does, it presupposes that the visceral impairment has remained silent so far. In this article, the exceptional case of a patient with systemic sarcoidosis revealed by unilateral exophthalmia is reported. Case presentation: The patient is a female with no history of substantial pathology. She had a unilateral right exophthalmia and ptosis evolving over 3 years. A dyspnea and dry cough were also reported with a duration of 1 year. The chest X-ray and CT scan revealed bilateral hilar opacities and mediastinal lymphadenopathy that lead to the suspicion of sarcoidosis. The cerebro-orbital CT scan led to the classification of the patient’s exophthalmia as Grade I and eliminated the possibility of other aetiologies. The mediastinoscopy indicated a granulomatous adenitis with no caseous necrosis, which allowed the diagnosis of a mediastinopulmonary sarcoidosis. Discussion and conclusion: The diagnostic approach to exophthalmia should involve a systematic search for sarcoidosis, although this aetiology remains exceptional.

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COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

10.21203/rs.3.rs-61891/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rajeev Kumar Singh ◽

Rohan Pandey ◽

Rishie Nandhan Babu

Keyword(s):

Deep Learning ◽

Model Performance ◽

Alternative Methods ◽

Model Complexity ◽

X Rays ◽

Kappa Score ◽

X Ray ◽

Pruning Strategy ◽

Chest X Ray ◽

To Come

Abstract COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delay in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel Deep Learning based solution to rapidly classify COVID -19 patient using chest X-Ray. The proposed solution uses image enhancement, image segmentation and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify Chest X-Ray into three classes viz. COVID-19, Pneumonia and Normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalisability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualisation in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state of the art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-Rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67\% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, Normal, and Pneumonia classes respectively on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold standard clinical and laboratory testing.

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