scholarly journals Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

2020 ◽  
Author(s):  
Rahul Kumar ◽  
Ridhi Arora ◽  
Vipul Bansal ◽  
Vinodh J Sahayasheela ◽  
Himanshu Buckchash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Rapid Development ◽  
Healthcare Systems ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Deep Feature ◽  
The World ◽  
Chest X Ray

ABSTRACTAccording to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

scholarly journals Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 651 ◽  
Author(s):  
Mohamed Loey ◽  
Florentin Smarandache ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Transfer Learning ◽  
Healthcare Systems ◽  
World Health ◽  
Transfer Model ◽  
X Rays ◽  
Computer Algorithms ◽  
X Ray ◽  
The Third ◽  
The World ◽  
Testing Accuracy

The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

scholarly journals SOM-LWL method for identification of COVID-19 on chest X-rays

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247176
Author(s):  
Ahmed Hamza Osman ◽  
Hani Moetque Aljahdali ◽  
Sultan Menwer Altarrazi ◽  
Ali Ahmed
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Genetic Material ◽  
Gain Control ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Identification Technique ◽  
Chest X Ray ◽  
Public Datasets

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

scholarly journals Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

2021 ◽  
Vol 12 (3) ◽  
pp. 011-019
Author(s):  
Haris Uddin Sharif ◽  
Shaamim Udding Ahmed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Infected Patient ◽  
World Health ◽  
X Rays ◽  
Popular Approach ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

scholarly journals ANALYZING CHEST X-RAY TO DIFFERENTIATE SYMPTOMS OF COVID-19 THROUGH ML APPROACH

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Deepali R Deshpande ◽  
Raj L Shah ◽  
Anish N Shaha
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Time Constraints ◽  
X Rays ◽  
Rt Pcr ◽  
X Ray ◽  
Social Distancing ◽  
Machine Learning Model ◽  
Chest X Ray

The motive behind the project is to build a machine learning model for detection of Covid-19. Using this model, it is possible to classify images of chest x-rays into normal patients, pneumatic patients, and covid-19 positive patients. This CNN based model will help drastically to save time constraints among the patients. Instead of relying on limited RT-PCR kits, just a simple chest x-ray can help us determine health of the patient. Not only we get immediate results, but we can also practice social distancing norms more effectively.

scholarly journals A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images

AI ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 418-435
Author(s):  
Khandaker Haque ◽  
Ahmed Abdelgawad
Keyword(s):  
Deep Learning ◽  
World Health ◽  
X Rays ◽  
X Ray ◽  
Global Pandemic ◽  
Proposed Model ◽  
Dental Diseases ◽  
Chest X Ray ◽  
Artery Disease ◽  
Health Organization

Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including coronary artery disease, malaria, Alzheimer’s disease, different dental diseases, and Parkinson’s disease. Like other cases, CNN has a substantial prospect in detecting COVID-19 patients with medical images like chest X-rays and CTs. Coronavirus or COVID-19 has been declared a global pandemic by the World Health Organization (WHO). As of 8 August 2020, the total COVID-19 confirmed cases are 19.18 M and deaths are 0.716 M worldwide. Detecting Coronavirus positive patients is very important in preventing the spread of this virus. On this conquest, a CNN model is proposed to detect COVID-19 patients from chest X-ray images. Two more CNN models with different number of convolution layers and three other models based on pretrained ResNet50, VGG-16 and VGG-19 are evaluated with comparative analytical analysis. All six models are trained and validated with Dataset 1 and Dataset 2. Dataset 1 has 201 normal and 201 COVID-19 chest X-rays whereas Dataset 2 is comparatively larger with 659 normal and 295 COVID-19 chest X-ray images. The proposed model performs with an accuracy of 98.3% and a precision of 96.72% with Dataset 2. This model gives the Receiver Operating Characteristic (ROC) curve area of 0.983 and F1-score of 98.3 with Dataset 2. Moreover, this work shows a comparative analysis of how change in convolutional layers and increase in dataset affect classifying performances.

COVID-19 Chest X-Ray Images Diagnosis: A Neutrosophic and Deep Transfer Learning Approach

2020 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Florentin Smarandache ◽  
Mohamed Loey
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
World Health ◽  
Transfer Model ◽  
X Ray ◽  
Proposed Model ◽  
The World ◽  
Chest X Ray ◽  
Testing Accuracy ◽  
Transfer Models

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a neutrosophic with a deep learning model for the detection of COVID-19 from chest X-ray medical digital images is presented. The proposed model relies on neutrosophic theory by converting the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images and they are, the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. Using neutrosophic images has positively affected the accuracy of the proposed model. The dataset used in this research has been collected from different sources as there is no benchmark dataset for COVID-19 chest X-ray until the writing of this research. The dataset consists of four classes and they are COVID-19, Normal, Pneumonia bacterial, and Pneumonia virus. After the conversion to the neutrosophic domain, the images are fed into three different deep transfer models and they are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures and they have been used with related work. To test the performance of the conversion to the neutrosophic domain, four scenarios have been tested. The first scenario is training the deep transfer models with True (T) neutrosophic images only. The second one is training on Indeterminacy (I) neutrosophic images, while the third scenario is training the deep models over the Falsity (F) neutrosophic images. The fourth scenario is training over the combined (T, I, F) neutrosophic images. According to the experimental results, the combined (T, I, F) neutrosophic images achieved the highest accuracy possible for the validation, testing and all performance metrics such Precision, Recall and F1 Score using Resnet18 as a deep transfer model. The proposed model achieved a testing accuracy with 78.70%. Furthermore, the proposed model using neutrosophic and Resnet18 had achieved superior testing accuracy with a related work which achieved 52.80% with the same experimental environmental setup and the same deep learning hyperparameters.

scholarly journals One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

2021 ◽  
Author(s):  
V. N. Manjunath Aradhya ◽  
Mufti Mahmud ◽  
D. S. Guru ◽  
Basant Agarwal ◽  
M. Shamim Kaiser
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Probabilistic Neural Network ◽  
Classification Model ◽  
Superior Performance ◽  
Generalized Regression Neural Network ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

AbstractCoronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

scholarly journals Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

2021 ◽  
Vol 9 ◽  
Author(s):  
Akshaya Karthikeyan ◽  
Akshit Garg ◽  
P. K. Vinod ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
High Sensitivity ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Healthcare Systems ◽  
Mortality Prediction ◽  
World Health ◽  
Patient Treatment ◽  
Public Healthcare ◽  
X Rays

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

scholarly journals CHEST X-RAY FINDINGS IN COVID-19 PNEUMONIA: A PICTORIAL REVIEW

2020 ◽  
pp. 9-11
Author(s):  
Zohra Ahmad ◽  
Parul Dutta ◽  
Deepjyoti Das Choudhury ◽  
Satabdi Kalita ◽  
Zohaib Hussain ◽  
...  
Keyword(s):  
Virus Disease ◽  
Vital Role ◽  
World Health ◽  
Thoracic Imaging ◽  
X Ray ◽  
Pictorial Review ◽  
Care Workers ◽  
The World ◽  
Chest X Ray ◽  
Health Organization

Corona Virus Disease 19 or COVID-19, was first detected in Wuhan province in China in December 2019 and reported to the World Health Organization (WHO) on December 31, 2019 [1]. It was declared a pandemic on March 11th, 2020 [2] and has till now affected 40 million people all around the world resulting in 1.1 million deaths (as of 18th Oct, 2020) [3]. As the world is reeling under the burden of the disease, it has been imperative for the radiologists to be familiar with the imaging appearance of the disease. Thoracic imaging with chest X-ray and CT is the key modality for the diagnosis and management of respiratory diseases. Although CT is more sensitive, the immense challenge of disinfection control in the modality may disrupt the service availability and portable X-ray may be considered to minimize the risk [4]. Use of portable X-ray has played a vital role in all the areas around the world during this pandemic. The purpose of this pictorial review is to represent the frequently encountered features and abnormalities in chest X-ray and strengthen the knowledge of the health-care workers in this war against the pandemic.

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