A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19

Sufian, Abu; Ghosh, Anirudha; Sadiq, Ali Safaa; Smarandache, Florentín

doi:10.36227/techrxiv.12535010.v1

Cited by 4 publications

(5 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The various security mechanisms are employed in sending and receiving data from these smart applications [ 12 ]. Smartphones can be used as input devices such as sensing, storing, and computing the results [ 13 ]. By the use of technology, it is possible to detect the COVID-19 suspects in early stages to eliminate the spread of infection.…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

Mir

Jamwal

Mehbodniya

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

show abstract

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

Mir

Jamwal

Mehbodniya

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…The ability to extract such features from non-medical images can be "transferred" to a medical image classification application, known as transfer learning [4]. The 3 pretrained CNNs used were ResNet50, VGG19 and VGG16, each of which were prepared in Python using Tensorflow and Keras [19].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…It is not exclusively required to develop a custom CNN to perform medical image diagnoses, however. Instead, a process known as transfer learning can be used, in which the feature extraction ability learned by a CNN trained on a different dataset can be transferred to assist in classifying images from a different application [4]. Zouch et al (2022) employed a CNN transfer learning approach, comparing the performance of the ResNet50 and VGG19 CNNs pretrained on the ImageNet dataset [5].…”

Section: Introductionmentioning

confidence: 99%

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Kuzinkovas

Clement

2022

Preprint

View full text Add to dashboard Cite

Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses from chest x-rays, and have extended this work to prove the feasibility of rapidly diagnosing COVID-19 to high degrees of accuracy. One issue in previous research has been the use of datasets containing only a few hundred images of chest x-rays containing COVID-19, causing CNNs to overfit the image data. This leads to a lower accuracy when the model attempts to classify new images, as would be clinically expected of it. In this work, we present a model trained on the COVID-QU-Ex dataset, overall containing 33,920 chest x-ray images, with an equal share of COVID-19, Non-COVID pneumonia, and Normal images. The model itself is an ensemble of pre-trained CNNs (ResNet50, VGG19, VGG16) and GLCM textural features. It achieved a 98.34% binary classification accuracy (COVID-19/no COVID-19) on a balanced test dataset of 6581 chest x-rays, and 94.68% for distinguishing between COVID-19, Non-COVID pneumonia and normal chest x-rays. Also, we herein discuss the effects of dataset size, demonstrating that a 98.82% 3-class accuracy can be achieved using the model if the training dataset only contains a few thousand images, but that generalisability of the model suffers with such small datasets.

show abstract

“…Next, [18] they optimized the transfer learning model by using twofold learning to improve accuracy in classifying chest X-rays on labels: COVID-19, viral pneumonia, and normal. The proposed model reportedly achieves 99.4% accuracy with an F1 score (0.994), in work [19] proposes a new anti-noise framework for the learning of noisy labels for segmentation tasks then proposes a new COVID-19 Pneumonia Lesions segmentation network (COPLE) -Net) to better deal with lesions of various scales and appearances, to make the training process noisy label resilient, they propose a noise-resistant Dice loss function and integrate it into a standalone ensemble framework and the result of this method is the noise function robust loss, and COPLE Net achieved higher performance than the advanced CNN for medical image segmentation, and work [20] reported that chest CT scan images were an effective screening strategy because they could reveal some cases of COVID-19 so it could be applied to shows symptoms in the early stages and pulmonary consolidation in the late stages.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of covid-19 based on CT Scan images using the convolution neural network

et al. 2021

View full text Add to dashboard Cite

The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN models

show abstract

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19

Abstract: This article has no data to published separately. All descriptions are mentioned in the article. This is a systematic review and discussion with possible solutions with deep transfer learning over the edge computing model.

Cited by 4 publications

References 73 publications

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Automatic detection of covid-19 based on CT Scan images using the convolution neural network

Contact Info

Product

Resources

About