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 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 Review on Deep Learning Based Automatic Detection of COVID-19 from Chest-X Ray Images

2021 ◽  
pp. 10-14
Author(s):  
Debaraj Rana ◽  
Swarna Prabha Jena ◽  
Subrat Kumar Pradhan
Keyword(s):  
Deep Learning ◽  
Virus Disease ◽  
Health Sector ◽  
Cost Effective ◽  
Data Set ◽  
X Ray ◽  
Time Consumption ◽  
Corona Virus ◽  
Global Pandemic ◽  
Chest X Ray

The Global pandemic declared Corona Virus Disease (COVID 19) has affected severely tothe health of human being over the globe. More than 15 crore around worldwide have been affected by the Novel Corona virus and it is progressing rapidly. Mainly in the health sector, the hospitals are not properly equipped with proper diagnosis system which can detect the disease accurately with less time consumption. The Chest X Ray image are taking less time and cost effective which can be used for detection of COVID 19 even the severity can also be determine form the CXR images. In the current research many researchers are focusing on implementation of Deep learning method for accurate and quick detection of COVID 19 which can help the radiologist for evaluation of the disease. In this review, proposed Deep learning methodology from the literature have been discussed with their experimental data set. This review could help to develop modified architecture which gives more improvement in the diagnosis in term of computational complexity and time consumption

scholarly journals Survey on Diagnosing CORONA VIRUS from Radiography Chest X-ray Images Using Convolutional Neural Networks

2022 ◽  
Author(s):  
J. T. Thirukrishna ◽  
Sanda Reddy Sai Krishna ◽  
Policherla Shashank ◽  
S. Srikanth ◽  
V. Raghu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
X Ray ◽  
Corona Virus ◽  
Chest X Ray

scholarly journals COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

2020 ◽  
Author(s):  
Antonios Makris ◽  
Ioannis Kontopoulos ◽  
Konstantinos Tserpes
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Bacterial Pneumonia ◽  
State Of The Art ◽  
Classification Performance ◽  
Black Swan ◽  
Healthy Individuals ◽  
X Ray ◽  
Chest X Ray

AbstractThe COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such “Black Swan” events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance can reach an accuracy of 95% for the best two models.

scholarly journals Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers

10.2196/18089 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e18089
Author(s):  
Ryoungwoo Jang ◽  
Namkug Kim ◽  
Miso Jang ◽  
Kyung Hwa Lee ◽  
Sang Min Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Computer Aided Diagnosis ◽  
X Ray ◽  
The Public ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets ◽  
Aided Diagnosis

Background Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors. Objective This study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling. Methods We trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist. Results In comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%. Conclusions The deep learning–based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning–based computer-aided diagnosis.

scholarly journals COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach

2021 ◽  
Vol 6 (1) ◽  
pp. 1-3
Author(s):  
Hala As'ad ◽  
Hilda Azmi ◽  
Pengcheng Xi ◽  
Ashkan Ebadi ◽  
Stéphane Tremblay ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Weighted Average ◽  
Learning Approach ◽  
Imbalanced Dataset ◽  
Deep Convolutional Neural Networks ◽  
Model Sensitivity ◽  
X Ray ◽  
Chest X Ray

COVID-19 pandemic has drastically changed our lives. Chest radiographyhas been used to detect COVID-19. However, the numberof publicly available COVID-19 x-ray images is extremely limited,resulting in a highly imbalanced dataset. This is a challenge whenusing deep learning for classification and detection. In this work, wepropose the use of pre-trained deep Convolutional Neural Networks(CNN) and integrate them with a few-shot learning approach namedimprinted weights. The integrated model is fine tuned to enhancethe capability of detecting COVID-19. The proposed solution thencombines the fine-tuned models using a weighted average ensemblefor achieving an optimal 82% sensitivity to COVID-19. To thebest of authors’ knowledge, the proposed solution is one of the firstto utilize imprinted weights model with weighted average ensemblefor enhancing the model sensitivity to COVID-19.

scholarly journals Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers (Preprint)

2020 ◽  
Author(s):  
Ryoungwoo Jang ◽  
Namkug Kim ◽  
Miso Jang ◽  
Kyung Hwa Lee ◽  
Sang Min Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Computer Aided Diagnosis ◽  
X Ray ◽  
The Public ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors. OBJECTIVE This study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling. METHODS We trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist. RESULTS In comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%. CONCLUSIONS The deep learning–based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning–based computer-aided diagnosis.

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