RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

Lydia, E. Laxmi; Anupama, C. S. S.; Beno, A.; Elhoseny, Mohamed; Alshehri, Mohammad Dahman; Selim, Mahmoud M.

doi:10.1007/s00500-021-06514-6

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…It can be advantageous for deploying disease detection systems in many ways, such as low latency, high speed, and can achieve economies of scale with the help of edge devices [ 8 ]. A review of previous works to deploy machine learning and deep learning models for COVID-19 detection and mitigation in edge environments [ 8 , 9 ] inspired the current study.…”

Section: Introductionmentioning

confidence: 99%

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

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Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.

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Section: Introductionmentioning

confidence: 99%

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

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“…They however do not report which edge devices used in their experiment. Laxmi Lydia et al ( 2021 ) developed a federated deep learning-based COVID-19 (FDL-COVID) detection model on edge computing environment. The method was based on SqueezeNet architecture.…”

Section: Results and Analysismentioning

confidence: 99%

Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray

2022

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Due to the outbreak of COVID-19 disease globally, countries around the world are facing shortages of resources (i.e. testing kits, medicine). A quick diagnosis of COVID-19 and isolating patients are crucial in curbing the pandemic, especially in rural areas. This is because the disease is highly contagious and can spread easily. To assist doctors, several studies have proposed an initial detection of COVID-19 cases using radiological images. In this paper, we propose an alternative method for analyzing chest X-ray images to provide an efficient and accurate diagnosis of COVID-19 which can run on edge devices. The approach acts as an enabler for the deep learning model to be deployed in practical application. Here, the convolutional neural network models which are fine-tuned to predict COVID-19 and pneumonia infection from chest X-ray images are developed by adopting transfer learning techniques. The developed model yielded an accuracy of 98.13%, sensitivity of 97.7%, and specificity of 99.1%. To highlight the important regions in the X-ray images which directs the model to its decision/prediction, we adopted the Gradient Class Activation Map (Grad-CAM). The generated heat maps from the Grad-CAM were then compared with the annotated X-ray images by board-certified radiologists. Results showed that the findings strongly correlate with clinical evidence. For practical deployment, we implemented the trained model in edge devices (NCS2) and this has achieved an improvement of 90% in inference speed compared to CPU. This shows that the developed model has the potential to be implemented on the edge, for example in primary care clinics and rural areas which are not well-equipped or do not have access to stable internet connections.

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“…Aljumah 46 provided a real‐time COVID‐19 ID&C system that uses the IoT platform to gather users' time‐sensitive symptom information to find coronaviruses early, monitor the clinical actions taken by survivors, and collect and analyze the necessary data to demonstrate the virus's existence. Lydia et al 47 proposed an IoT‐enabled FDL‐COVID model. SqueezeNet is used to generate the DL model from IoT patient data.…”

Section: Preliminariesmentioning

confidence: 99%

IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

et al. 2023

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Performing a coronary disease diagnosis remotely is challenging now‐a‐days. COVID‐19 is a worldwide pandemic, and methods for detecting COVID‐19 are hampered by insufficient data and a lack of validation testing. Internet of Things (IoT) applications that rely on cloud computing (CC) are being studied in an effort to improve e‐Healthcare systems, even though CC presents substantial latency, bandwidth, energy consumption, security and privacy issues and so forth. The extension to CC, fog computing (FC), can overcome these said limitations. This study aims to diagnose COVID‐19 patients to fight the outbreak remotely. This study proposes IFCnCov, which enables remote users to diagnose COVID‐19 disease in real‐time using integrated IoT, FC, and CC principles, as well as ensemble learning (EL) and deep learning (DL). The proposed system is a two‐layered architecture, trained with DL approaches to two different datasets: a symptom‐based dataset and a chest x‐rays imaging dataset obtained from the Kaggle repository employing several evaluative measures. From various experiments, this proposed IFCnCov achieves comparatively enhanced accuracies of 97.71% and 98.64%, precision of 97.38% and 98.52%, sensitivity of 98.19% and 98.92%, specificity of 97.21% and 98.34%, and F1‐scores of 97.79% and 98.72% in the first and second stages respectively, which also outperforms some other considered state‐of‐the‐art works. Additionally, this work is validated in terms of several network parameters, including scalability, energy consumption, network utilization, jitter, processing time, throughput, and arbitration time.

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RETRACTED ARTICLE: Cognitive computing-based COVID-19 detection on Internet of things-enabled edge computing environment

Cited by 13 publications

References 18 publications

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray

IFCnCov: An IoT‐based smart diagnostic architecture for COVID‐19

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