PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using deep convolutional neural network models

Misra, Priyavrat; Panigrahi, Niranjan; Patro, S Gopal Krishna; Salau, Ayodeji Olalekan; Aravinth, S. S.

doi:10.1007/s11042-023-16084-4

Cited by 16 publications

(2 citation statements)

References 31 publications

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“…Prasad et al 30 developed a method that detects COVID-19 from chest CT images using deep learning and cloud-based image analysis for priority-wise distribution of COVID-19 vaccination. P. Misra et al 31 applied pre-trained DenseNet121, ResNet18, and VGG16 for a parallel ensemble bagging-based model to detect COVID-19 from chest X-rays. R. Kumar et al 32 proposed a model that optimally reduces features extracted from GoogLeNet and ResNet152 using the Pearson correlation coefficient and uses an XGBoost classifier for detecting COVID-19, pneumonia, and normal from chest X-ray.…”

Section: Literature Reviewmentioning

confidence: 99%

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

Saha,

Nandi

2024

DIGITAL HEALTH

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Aim To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods. Methods and Materials The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation. Results Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT. Conclusion Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.

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Section: Literature Reviewmentioning

confidence: 99%

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

Saha,

Nandi

2024

DIGITAL HEALTH

View full text Add to dashboard Cite

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“…Another important finding from Table 1 is that most studies relied on blood tests and X-rays taken after the disease was confirmed, indicating that their primary focus was on diagnosing COVID-19 after symptoms appeared, such as [ 31 , 33 , 34 ]. On the other hand, studies [ 53 – 55 ], and [ 57 ] have adopted a different strategy and focused on the early diagnosis of SARS-CoV-2, which represents a clear departure in methodology and emphasis from the field’s general tendency.…”

Section: Related Workmentioning

confidence: 99%

A tree-based explainable AI model for early detection of Covid-19 using physiological data

Talib,

Afadar,

Nasir

et al. 2024

BMC Med Inform Decis Mak

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With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git.

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COVID-19 and flu diagnosis from short electrocardiogram images using a residual neural network

Kwon,

Lee,

Yoon

et al. 2025

Biomedical Signal Processing and Control

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PETLFC: Parallel ensemble transfer learning based framework for COVID-19 differentiation and prediction using deep convolutional neural network models

Cited by 16 publications

References 31 publications

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images

A tree-based explainable AI model for early detection of Covid-19 using physiological data

COVID-19 and flu diagnosis from short electrocardiogram images using a residual neural network

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