scholarly journals Diagnosis of COVID-19 Using Chest X-ray

2021 ◽  
Vol 2 (1) ◽  
pp. 55-64
Author(s):  
Sumit Malik ◽  
◽  
Shivendra Singh ◽  
Narendra Mohan Singh ◽  
Naman Panwar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Study ◽  
Imaging Technique ◽  
Respiratory Organ ◽  
X Ray ◽  
Chest X Ray ◽  
Organ Infection ◽  
Infective Agent

Covid-19 is also a wide spreading infective agent disease that infects humans. A clinical study of COVID-19 infected patients has shown that these kinds of patients are square measure principally infected from a respiratory organ infection when come in contact with this disease. Chest xray (i.e., radiography) a less complicated imaging technique for identification respiratory organ connected issues. Deep learning is that the foremost undefeated technique of machine learning, that provides helpful analysis to review an oversize quantity of chest x-ray pictures which may critically impact on screening of Covid-19. Throughout this work, we have taken the PA read of chest x-ray scans for covid-19 affected patients conjointly as healthy patients. We have used deep learning-based CNN models and compared their performance. We have equate ResNeXt models and inspect their precision to investigate the model presentation, 6432 chest x-ray scans samples square measure collected from the Kaggle repository. This work solely core on potential ways of cluster covid-19 infected patients.

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

scholarly journals CoviDecode : Detection of COVID-19 from Chest X-Ray images using Convolutional Neural Networks

2020 ◽  
Vol 6 (12) ◽  
pp. 436-439
Author(s):  
Rishabh Raj
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Virus Disease ◽  
X Ray ◽  
Corona Virus ◽  
Life Saving ◽  
Chest X Ray

ommand, product recommendation and medical diagnosis. The detection of severe acute respiratory syndrome corona virus 2 (SARS CoV-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for bothpatients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images were used in the experiments, which involved the training of deep learning and machine learning classifiers. Experiments were performed using convolutional neural networks and machine learning models. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean accuracy of 98.50%. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID- 19 in a limited number of, and in imbalanced, chest X-rayimages.

scholarly journals Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

2021 ◽  
Vol 11 (10) ◽  
pp. 993
Author(s):  
Roberta Fusco ◽  
Roberta Grassi ◽  
Vincenza Granata ◽  
Sergio Venanzio Setola ◽  
Francesca Grassi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Standard Deviation ◽  
Mean Value ◽  
Learning Approaches ◽  
Inclusion Criteria ◽  
X Ray ◽  
Standard Deviation Range ◽  
Chest X Ray

Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

scholarly journals Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Hossein Mohammad-Rahimi ◽  
Mohadeseh Nadimi ◽  
Azadeh Ghalyanchi-Langeroudi ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Chest Ct ◽  
Accurate Diagnosis ◽  
Ct Scans ◽  
Rt Pcr ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Chest X Ray

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

scholarly journals Ensemble deep learning for tuberculosis detection

2020 ◽  
Vol 17 (2) ◽  
pp. 1014
Author(s):  
Mohd Hanafi Ahmad Hijazi ◽  
Leong Qi Yang ◽  
Rayner Alfred ◽  
Hairulnizam Mahdin ◽  
Razali Yaakob
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Lung Cancer ◽  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Tubercle Bacillus ◽  
X Ray ◽  
Initial Stage ◽  
The World ◽  
Chest X Ray

Tuberculosis (TB) is one of the deadliest infectious disease in the world. TB is caused by a type of tubercle bacillus called Mycobacterium Tuberculosis. Early detection of TB is pivotal to decrease the morbidity and mortality. TB is diagnosed by using the chest x-ray and a sputum test. Challenges for radiologists are to avoid confused and misdiagnose TB and lung cancer because they mimic each other. Semi-automated TB detection using machine learning found in the literature requires identification of objects of interest. The similarity of tissues, veins and small nodules presenting the image at the initial stage may hamper the detection. In this paper, an approach to detect TB, that does not require segmentation of objects of interest, based on ensemble deep learning, is presented. Evaluation on publicly available datasets show that the proposed approach produced a model that recorded the best accuracy, sensitivity and specificity of 91.0%, 89.6% and 90.7% respectively.

scholarly journals The application of Hybrid deep learning Approach to evaluate chest ray images for the diagnosis of pneumonia in children

2020 ◽  
Author(s):  
Mohammad Ali Abbasa ◽  
Syed Usama Khalid Bukhari ◽  
Syed Khuzaima Arssalan Bokhari ◽  
manal niazi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Morbidity And Mortality ◽  
Hybrid Architecture ◽  
X Ray ◽  
Light Gradient ◽  
Study Results ◽  
Chest X Ray ◽  
Hybrid Machine

AbstractBackgroundPneumonia is a leading cause of morbidity and mortality worldwide, particularly among the developing nations. Pneumonia is the most common cause of death in children due to infectious etiology. Early and accurate Pneumonia diagnosis could play a vital role in reducing morbidity and mortality associated with this ailment. In this regard, the application of a new hybrid machine learning vision-based model may be a useful adjunct tool that can predict Pneumonia from chest X-ray (CXR) images.Aim & Objectivewe aimed to assess the diagnostic accuracy of hybrid machine learning vision-based model for the diagnosis of Pneumonia by evaluating chest X-ray (CXR) imagesMaterials & MethodsA total of five thousand eight hundred and fifty-six digital X-ray images of children from ages one to five were obtained from the Chest X-Ray Pneumonia dataset using the Kaggle site. The dataset contains fifteen hundred and eighty-three digital X-ray images categorized as normal, where four thousand two hundred and seventy-three digital X-ray images are categorized as Pneumonia by an expert clinician. In this research project, a new hybrid machine learning vision-based model has been evaluated that can predict Pneumonia from chest X-ray (CXR) images. The proposed model is a hybrid of convolutional neural network and tree base algorithms (random forest and light gradient boosting machine). In this study, a hybrid architecture with four variations and two variations of ResNet architecture are employed, and a comparison is made between them.ResultsIn the present study, the analysis of digital X-ray images by four variations of hybrid architecture RN-18 RF, RN-18 LGBM, RN-34 RF, and RN-34 LGBM, along with two variations of ResNet architecture, ResNet-18 and ResNet-30 have revealed the diagnostic accuracy of 97.78%, 96.42%, 97.1%,96.59%, 95.05%, and 95.05%, respectively.DiscussionThe analysis of the present study results revealed more than 95% diagnostic accuracy for the diagnosis of Pneumonia by evaluating chest x-ray images of children with the help of four variations of hybrid architectures and two variations of ResNet architectures. Our findings are in accordance with the other published study in which the author used the deep learning algorithm Chex-Net with 121 layers.ConclusionThe hybrid machine learning vision-based model is a useful tool for the assessment of chest x rays of children for the diagnosis of Pneumonia.

scholarly journals Classificação de Imagens de Raio-x de Torax com Reconhecimento Visual da IBM Cloud para Diagnóstico de Pneumonia

2019 ◽  
Author(s):  
Raianny Proença de Camargo De Oliveira ◽  
Guilherme Rodrigues Sganderla ◽  
Claudio Roberto Marquetto Maurício ◽  
Fabiana Frata Furlan Peres
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Recognition ◽  
X Ray ◽  
Chest X Ray ◽  
Ibm Watson

A capacidade de aprender por meio de exemplos e formular predições são as principais características do Machine Learning, uma subárea da inteligência artificial. Existem diversos frameworks disponíveis que utilizam Machine Learning para solução dos mais variados tipos de problemas, como para reconhecer e classificar objetos em uma imagem. Utilizando os serviços fornecidos por IBM Watson Visual Recognition que emprega algoritmos de deep learning, uma subárea de Machine learning, um modelo foi criado e aplicado no dataset Chest X-Ray Images for Classification. Os resultados obtidos com o modelo criado foram comparados com a classificação geral fornecida pela IBM. Os serviços utilizados da Watson Visual Recognition são os disponibilizados para o plano do tipo Lite, um plano gratuito. Este trabalho discute como esta limitação afetou os resultados e descreve a eficiência da ferramenta nesta versão. Mesmo com as limitações o modelo obtido reconheceu corretamente um pulmão saudável em 75% das imagens de teste e classificou corretamente 93,34% das imagens de radiografias de tórax que retratam pneumonia.

scholarly journals Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches

2020 ◽  
Author(s):  
Khair Ahammed ◽  
Md. Shahriare Satu ◽  
Mohammad Zoynul Abedin ◽  
Md. Auhidur Rahaman ◽  
Sheikh Mohammed Shariful Islam
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Sampling ◽  
Learning Approaches ◽  
X Ray ◽  
Learning Procedure ◽  
Community Transmission ◽  
Chest X Ray ◽  
Single Dataset ◽  
F Measure

AbstractThis study aims to investigate if applying machine learning and deep learning approaches on chest X-ray images can detect cases of coronavirus. The chest X-ray datasets were obtained from Kaggle and Github and pre-processed into a single dataset using random sampling. We applied several machine learning and deep learning methods including Convolutional Neural Networks (CNN) along with classical machine learners. In deep learning procedure, several pre-trained models were also employed transfer learning in this dataset. Our proposed CNN model showed the highest accuracy (94.03%), AUC (95.52%), f-measure (94.03%), sensitivity (94.03%) and specificity (97.01%) as well as the lowest fall out (4.48%) and miss rate (2.98%) respectively. We also evaluated specificity and fall out rate along with accuracy to identify non-COVID-19 individuals more accurately. As a result, our new models might help to early detect COVID-19 patients and prevent community transmission compared to traditional methods.

Sign in / Sign up

Export Citation Format

Share Document