Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images

Alhudhaif, Adi; Polat, Kemal; Karaman, Onur

doi:10.1016/j.eswa.2021.115141

Cited by 71 publications

(31 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…COVID-19 has been one of the strongest health problems in the last two years and is an area of interest not only in the medical area but also in machine learning. Classification algorithms, such as convolutional neural networks, have been widely used to provide a useful diagnosis to healthcare professionals [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Previous Workmentioning

confidence: 99%

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

et al. 2021

View full text Add to dashboard Cite

Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task.

show abstract

Section: Previous Workmentioning

confidence: 99%

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Moreover, a computer-aided diagnostic combined approach based on graph CNN and pre-trained CNN model (Kumar et al [19]), a deep Covix-net model (Vinod et al [20]), new deep hybrid and deep boosted hybrid learning models (Khan et al [21]), and a gradient weighted class activation mapping technique (Panwar et al [22]) for coronavirus detection exhibited accuracies of 97.60%, 97%, 98.53%, and 95%, respectively. Likewise, a DenseNet-201 architecture reported by Alhudhaif et al [23], a PSO-based eXtreme Gradient Boosting model recommended by Dias Júnior et al [24], an automatic AI-based system using majority voting ensemble techniques suggested by Chandra et al [25], and a bi-level prediction model by Das et al [26] exhibited respective accuracies of 94.96%,98.71%, 91.329%, and96.74%to diagnose COVID-19. Other novel techniques to detect coronavirus include a deep LSTM model (Demir et al [27]), Inception-v3 model based on deep CNN associated with Multi-Layered Perceptron model called CovScanNet (Sait et al [28]), and a hybrid deep CNN technique with discrete wavelet transform features (Mostafiz et al [29].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

2021

View full text Add to dashboard Cite

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

show abstract

“…Scientists at the Centers for Disease Control (CDC) and World Health Organizations have identified common symptoms of this disease including dry cough, headache, difficulty breathing, weakness, and lack of smell and taste. Moreover, it has been found in some extreme cases, dyspnea and/or hypoxemia can occur a week after the appearance of the disease, accompanied by septic shock, acute respiratory distress syndrome (ARDS), and dysfunction of coagulation 6 . These different modes exacerbated the rapid spread of the virus and made it highly difficult to control the spread.…”

Section: Introductionmentioning

confidence: 99%

E.Co.Tech-electrochemical handheld breathalyzer COVID sensing technology

Banga

Paul

France³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Breathomics is widely emerging as a strategy for non-invasive diagnosis of respiratory inflammation. In this study, we have evaluated the metabolic signals associated with Coronavirus (SARS COV-2), mainly the release of nitric oxide in breath. We have demonstrated the utility of a breath analyzer-based sensor platform for the detection of trace amounts of this target species. The sensor surface is modified with Room Temperature Ionic Liquid (RTIL) that allows faster diffusion of the target gas and can be used for gas sensing application. A low limit of detection (LOD) of 50 parts per billion has been achieved with a 95% confidence interval for detection of nitric oxide.. This inhouse designed sensor is incorporated into a breath analyzer system that displays enhanced sensitivity, specificity, linearity, and reproducibility for NO gas monitoring. The developed sensor platform can detect target concentrations of NO ranging from 50 to 250 ppb, using 1-Ethyl-3-methylimidazolium Tetrafluoroborate ([EMIM]BF4) as RTIL and displays fast response time of 5 s, thereby allowing easy detection of the target gas species. The sensor successfully quantifies the diffusion current and charge modulations arising within the electrical double layer from the RTIL–NO interactions through DC-based chronoamperometry (CA). The subjects tested negative and positive are significantly different (p < 0.01). The prototype can potentially be used for human health monitoring and screening, especially during the pandemic due to its portability, small size, an embedded RTIL sensing element, integrability with a low-power microelectronic device, and an IoT interface.

show abstract

Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images

Cited by 71 publications

References 49 publications

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

Classification of Diseases Using Machine Learning Algorithms: A Comparative Study

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

E.Co.Tech-electrochemical handheld breathalyzer COVID sensing technology

Contact Info

Product

Resources

About