scholarly journals CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259179
Author(s):  
M. Rubaiyat Hossain Mondal ◽  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Classification Accuracy ◽  
Performance Metrics ◽  
Ct Images ◽  
Chest Ct ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Recall Accuracy ◽  
X Ray ◽  
Normal People ◽  
Run Up

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

scholarly journals Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Arnab Kumar Mishra ◽  
Sujit Kumar Das ◽  
Pinki Roy ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Performance Metrics ◽  
Decision Fusion ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Crisis ◽  
Potential Applicability ◽  
Deep Cnn ◽  
Polymerase Chain ◽  
High Utility

Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.

scholarly journals COVID-19 Diagnosis with Deep Learning

2021 ◽  
Vol 42 (1) ◽  
pp. e88825
Author(s):  
Hatice Catal Reis
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Performance Metrics ◽  
Binary Classification ◽  
Network Models ◽  
Ct Images ◽  
Chest Ct ◽  
Point Of View ◽  
Neural Network Models ◽  
Convolutional Network

The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images).  The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC.  Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.

CNN-based Prediction of COVID-19 using Chest CT Images

2021 ◽  
pp. 2250039
Author(s):  
Tanvi Arora
Keyword(s):  
Ct Scan ◽  
Good Accuracy ◽  
Ct Images ◽  
Chest Ct ◽  
X Ray ◽  
Infected People ◽  
The World ◽  
Severity Of The Disease ◽  
Application Programs ◽  
Chest Ct Scan

The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.

scholarly journals Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

2021 ◽  
Vol 11 (15) ◽  
pp. 7004
Author(s):  
Shreya Biswas ◽  
Somnath Chatterjee ◽  
Arindam Majee ◽  
Shibaprasad Sen ◽  
Friedhelm Schwenker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Respiratory Diseases ◽  
Ct Images ◽  
Chest Ct ◽  
The Novel ◽  
Proposed Model ◽  
The World ◽  
Prediction Capability ◽  
Efficient Transfer

The novel SaRS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high F1-score of 0.99 on the publicly available SaRS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.

scholarly journals ÍNDICE RELAÇÃO CINTURA-ESTATURA PARA PREDIÇÃO DO EXCESSO DE PESO EM CRIANÇAS

2017 ◽  
Vol 36 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Sarah Aparecida Vieira ◽  
Andréia Queiroz Ribeiro ◽  
Helen Hermana Miranda Hermsdorff ◽  
Patrícia Feliciano Pereira ◽  
Silvia Eloiza Priore ◽  
...  
Keyword(s):  
Dual Energy ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
X Ray ◽  
Receiver Operating

RESUMO Objetivo: Identificar um indicador de adiposidade abdominal de baixo custo e com maior acurácia para predizer o excesso de peso em crianças de quatro a sete anos idade. Métodos: Estudo transversal com amostra de 257 crianças de 4 a 7 anos. Os indicadores de adiposidade abdominal avaliados foram: perímetro da cintura (PC), relação cintura-estatura (RCE) e percentual de gordura central (avaliado pela técnica dual energy X-ray absorptiometry - DEXA). O excesso de peso foi classificado pelo índice de massa corporal por idade (IMC/I). Nas análises, estimou-se a razão de prevalência (RP) pela regressão de Poisson com variância robusta e utilizou-se a curva (receiver operating characteristics - ROC), considerando como significância estatística p<0,05. Resultados: A prevalência de excesso de peso foi de 24,9%, e observou-se maior mediana dos indicadores de adiposidade abdominal no grupo de crianças com excesso de peso. As crianças com valores aumentados de PC (RP=4,1; IC95% 2,86-5,86), RCE (RP=5,76; IC95% 4,14-8,02) e percentual de gordura central (RP=2,48; IC95% 1,65-3,73) apresentaram maior prevalência de excesso de peso. Verificou-se, na análise de curva ROC, que o índice RCE apresentou maior área sob a curva, comparado ao PC e ao percentual de gordura central estimada pelo DEXA, na predição do excesso de peso. Conclusões: Diante dos resultados, sugere-se a utilização da RCE para triagem de crianças com excesso de peso.

Kriteriumsbezogene Cut-Off-Werte für Tätigkeitsspielraum und Arbeitsintensität

Diagnostica ◽  
2019 ◽  
Vol 65 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Vincent Mustapha ◽  
Renate Rau
Keyword(s):  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Receiver Operating

Zusammenfassung. Cut-Off-Werte ermöglichen eine ökonomische, binäre Beurteilung von Summenscores. Für Beanspruchungsfragebögen, die personenbezogene Merkmale erfragen, sind Cut-Off-Werte häufig vorhanden und in der klinischen Diagnostik unerlässlich. Für die Bewertung von Arbeitsmerkmalen sind Cut-Off-Werte ebenfalls wünschenswert. Bislang fehlen sie jedoch für die Beurteilung von Arbeitsmerkmalen wie Arbeitsintensität und Tätigkeitsspielraum. Zwischen 2006 und 2016 wurden daher in verschiedenen Branchen 801 objektive Arbeitsplatzanalysen durchgeführt, welche eine Unterteilung in gut und schlecht gestalteten Tätigkeitsspielraum sowie gut und schlecht gestaltete Arbeitsintensität nach DIN EN ISO 6385 (2016) ermöglichen. Anhand dieser Unterteilung wurden mit der Receiver-Operating-Characteristics-Analyse Cut-Off-Werte für den subjektiv-bedingungsbezogen Fragebogen zum Erleben von Arbeitsintensität und Tätigkeitsspielraum (FIT; Richter et al., 2000 ) ermittelt. Für den Tätigkeitsspielraum weisen Summenscores ≤ 22 und für die Arbeitsintensität Summenscores ≥ 15 auf eine schlechte Gestaltung des jeweiligen Arbeitsmerkmals hin. Anhand einer weiteren Stichprobe von 1 076 Arbeitenden konnte gezeigt werden, dass Arbeitende mit schlecht gestaltetem Tätigkeitspielraum vital erschöpfter sowie weniger engagiert sind und Arbeitende mit schlecht gestalteter Arbeitsintensität eine höhere Erholungsunfähigkeit sowie vitale Erschöpfung aufweisen.

Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians

1991 ◽  
Vol 30 (03) ◽  
pp. 187-193 ◽  
Author(s):  
H. J. Moens ◽  
J. K. van der Korst
Keyword(s):  
Receiver Operating Characteristics ◽  
Bayesian Decision ◽  
Operating Characteristics ◽  
Likelihood Ratios ◽  
Improve Performance ◽  
Mathematical Basis ◽  
Human Expert ◽  
Test Population ◽  
Sensitivity Specificity ◽  
Diagnostic Outcome

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.

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