SARS n-CoV2-19 detection from chest x-ray images using deep neural networks

2020 ◽  
Vol 16 (5) ◽  
pp. 419-427 ◽  
Author(s):  
Mohammad Khalid Pandit ◽  
Shoaib Amin Banday
Keyword(s):  
Health Care Professionals ◽  
Low Cost ◽  
Fine Tuning ◽  
Medical Community ◽  
X Rays ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Chest X Ray ◽  
Novel Coronavirus

Purpose Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n-CoV2 is known to affect the lungs of the COVID-19 positive patients. Chest x-rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID-19 using chest x-rays. Design/methodology/approach The authors used a data set containing confirmed COVID-19 positive, common bacterial pneumonia and healthy cases (no infection). A collection of 1,428 x-ray images is used in this study. The authors used a pre-trained VGG-16 model for the classification task. Transfer learning with fine-tuning was used in this study to effectively train the network on a relatively small chest x-ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID-19 detection. Findings The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x-ray images as possible diagnostic tool for faster COVID-19 detection to complement the already testing and diagnosis methods. Originality/value The proposed method can be used as initial screening which can help health-care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

scholarly journals Detecting Coronavirus from Chest X-rays Using Transfer Learning

COVID ◽  
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.

scholarly journals Deep Learning for Screening COVID-19 using Chest X-Ray Images

2020 ◽  
Author(s):  
Sanhita Basu ◽  
Sushmita Mitra ◽  
Nilanjan Saha
Keyword(s):  
Deep Learning ◽  
Lung Parenchyma ◽  
Clinical Findings ◽  
Data Sets ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Increasing Demand ◽  
Novel Coronavirus ◽  
Diverse Data

AbstractWith the ever increasing demand for screening millions of prospective “novel coronavirus” or COVID-19 cases, and due to the emergence of high false negatives in the commonly used PCR tests, the necessity for probing an alternative simple screening mechanism of COVID-19 using radiological images (like chest X-Rays) assumes importance. In this scenario, machine learning (ML) and deep learning (DL) offer fast, automated, effective strategies to detect abnormalities and extract key features of the altered lung parenchyma, which may be related to specific signatures of the COVID-19 virus. However, the available COVID-19 datasets are inadequate to train deep neural networks. Therefore, we propose a new concept called domain extension transfer learning (DETL). We employ DETL, with pre-trained deep convolutional neural network, on a related large chest X-Ray dataset that is tuned for classifying between four classes viz. normal, other_disease, pneumonia and Covid — 19. A 5-fold cross validation is performed to estimate the feasibility of using chest X-Rays to diagnose COVID-19. The initial results show promise, with the possibility of replication on bigger and more diverse data sets. The overall accuracy was measured as 95.3% ± 0.02. In order to get an idea about the COVID-19 detection transparency, we employed the concept of Gradient Class Activation Map (Grad-CAM) for detecting the regions where the model paid more attention during the classification. This was found to strongly correlate with clinical findings, as validated by experts.

scholarly journals A Holistic Approach to Identification of Covid-19 Patients from Chest X-Ray Images utilizing Transfer Based Learning

2020 ◽  
Author(s):  
Taki Hasan Rafi
Keyword(s):  
Holistic Approach ◽  
Neural System ◽  
Medical Community ◽  
Test Cases ◽  
Testing Time ◽  
Analysis Framework ◽  
Rt Pcr ◽  
X Ray ◽  
Chest X Ray ◽  
Novel Coronavirus

Novel coronavirus likewise called COVID-19 began in Wuhan, China in December 2019 and has now outspread over the world. Around 8 millions of individuals previously got influenced by novel coronavirus and it causes at any rate 500,000 deaths. There are just about 90,000 individuals contaminated by COVID-19 in Bangladesh too. As it is an exceptionally new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for lower incoming countries to test cases easily. RT-PCR test is the most generally utilized analysis framework for COVID-19 patient detection. However, by utilizing X-ray image based programmed recognition can diminish the expense and testing time. So according to handling this test, it is important to program and effective recognition to forestall transmission to others. In this paper, we attempt to distinguish COVID-19 patients by chest X-ray images. We execute different pre-trained deep neural system models, for example, Sequential, DenseNet121, ResNet152 and EfficientNetB4 to assess the most productive outcome. And aims to utilize transfer-based learning. We assess this outcome by AUC, where EfficientNetB4 has 0.997 AUC, ResNet50 has 0.967 AUC, DenseNet121 has 0.874 AUC and the Sequential model has 0.762 AUC individually. And EfficientNetB4 has achieved 98.86% accuracy.

scholarly journals COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

2021 ◽  
Vol 18 (21) ◽  
pp. 11086
Author(s):  
Debaditya Shome ◽  
T. Kar ◽  
Sachi Nandan Mohanty ◽  
Prayag Tiwari ◽  
Khan Muhammad ◽  
...  
Keyword(s):  
Data Science ◽  
Disease Spread ◽  
Classification Task ◽  
Data Sets ◽  
Sudden Increase ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Transformer Model

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient’s X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

scholarly journals IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

2020 ◽  
Author(s):  
Juliana C. Gomes ◽  
Valter A. de F. Barbosa ◽  
Maira A. Santana ◽  
Jonathan Bandeira ◽  
Mêuser Jorge Silva Valença ◽  
...  
Keyword(s):  
Intelligent System ◽  
Low Cost ◽  
Computational Cost ◽  
Specific Treatment ◽  
Support Vector ◽  
X Rays ◽  
X Ray ◽  
Average Accuracy ◽  
Vector Machines ◽  
Chest X Ray

AbstractIn late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

scholarly journals Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8219
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Sultan Ahmad ◽  
Shakir Khan ◽  
Mohammed Ali Alshara ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
High Performance ◽  
Data Augmentation ◽  
Early Stage ◽  
Accurate Diagnosis ◽  
Fine Tuning ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

scholarly journals Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach

2020 ◽  
Author(s):  
Sifat Ahmed ◽  
Tonmoy Hossain ◽  
Oishee Bintey Hoque ◽  
Sujan Sarker ◽  
Sejuti Rahman ◽  
...  
Keyword(s):  
High Resolution ◽  
Well Being ◽  
X Rays ◽  
X Ray ◽  
Pcr Diagnosis ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
And Control ◽  
Learning Architectures ◽  
Global Population

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the noninvasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

scholarly journals Transfer learning to detect COVID-19 automatically from X-ray images, using convolutional neural networks

2020 ◽  
Author(s):  
Mundher Taresh ◽  
Ningbo Zhu ◽  
Talal Ahmed Ali Ali
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Classification Model ◽  
Statistical Parameters ◽  
Data Set ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
Novel Coronavirus

AbstractNovel coronavirus pneumonia (COVID-19) is a contagious disease that has already caused thousands of deaths and infected millions of people worldwide. Thus, all technological gadgets that allow the fast detection of COVID-19 infection with high accuracy can offer help to healthcare professionals. This study is purposed to explore the effectiveness of artificial intelligence (AI) in the rapid and reliable detection of COVID-19 based on chest X-ray imaging. In this study, reliable pre-trained deep learning algorithms were applied to achieve the automatic detection of COVID-19-induced pneumonia from digital chest X-ray images.Moreover, the study aims to evaluate the performance of advanced neural architectures proposed for the classification of medical images over recent years. The data set used in the experiments involves 274 COVID-19 cases, 380 viral pneumonia, and 380 healthy cases, which was collected from the available X-ray images on public medical repositories. The confusion matrix provided a basis for testing the post-classification model. Furthermore, an open-source library PyCM* was used to support the statistical parameters. The study revealed the superiority of Model VGG16 over other models applied to conduct this research where the model performed best in terms of overall scores and based-class scores. According to the research results, deep learning with X-ray imaging is useful in the collection of critical biological markers associated with COVID-19 infection. The technique is conducive for the physicians to make a diagnosis of COVID-19 infection. Meanwhile, the high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis.

Chest X-ray interpretation

2019 ◽  
Vol 14 (5) ◽  
pp. 1-9
Author(s):  
Susan Dawkes ◽  
Michelle O'Reilly
Keyword(s):  
Cardiovascular Disease ◽  
Low Cost ◽  
Disease Diagnosis ◽  
Medical Intervention ◽  
X Rays ◽  
Early Medical Intervention ◽  
X Ray ◽  
Anatomy And Physiology ◽  
Patient Will ◽  
Chest X Ray

The chest X-ray is a common, low-cost investigation that is an important aid in cardiovascular disease diagnosis. Although newer, more sophisticated modalities of imaging are available, chest X-rays remain fundamental, first-line investigations used to determine patient care. Although a radiologist should report all X-rays, nurses and other health professionals frequently examine and interpret chest X-rays. A sound knowledge of normal anatomy and physiology is fundamental. The technical quality of the chest X-ray, specifically the projection of the film, its orientation, rotation of the patient and penetration of the X-rays is important to determine. To prevent abnormalities going undetected when examining chest X-rays, a comprehensive systematic approach to assessment and interpretation is essential. Common abnormalities seen on X-rays from patients with cardiovascular disease have specific differentiating features and these, combined with the physical examination of the patient, will allow for faster diagnosis and early medical intervention.

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