Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment

Abdulkareem, Karrar Hameed; Mohammed, Mazin Abed; Salim, Ahmad; Arif, Muhammad; Geman, Oana; Gupta, Deepak; Khanna, Ashish

doi:10.1109/jiot.2021.3050775

Cited by 168 publications

(127 citation statements)

References 28 publications

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“…These studies suggested a method to protect sensor data and enhances the performance of big-data analysis in the system. Dynamic and adaptive optimization heuristics, such as genetic algorithm and reinforcement aware schemes, were suggested [2,5,23,[25][26][27]. The different objectives were obtained, such as the cost, security, response time and energy of sensors devices during offloading and scheduling in the IoMT network.…”

Section: Related Workmentioning

confidence: 99%

“…Fog-cloud is a cooperative computing network where remote cloud offers via internet and fog computing provide services at the edge of the network [4]. Many healthcare applications have been developed based on the IoMT system, where many patients practised these applications with mobile devices [5]. Simultaneously, healthcare sensors are directly connected to the fog-cloud servers to perform any healthcare task for patients [6].…”

Section: Introductionmentioning

confidence: 99%

“…Input :5 CH ← data i ← pk ← Tms, k1, j1 to K, M; 6 if (status ← PH = CH) then12 Assign each task to one node at a time, based on Equation (4);13 …”

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confidence: 99%

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Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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“…The model exhibited an accuracy between 94 and 96%. K. H. Abdulkareem et al [ 27 ] proposed a machine learning and IoT based framework to diagnose COVID-19. The study uses Naïve Bayes, Support Vector Machine and random forest for the classification task and reports the highest achieved accuracy (SVM) to be 95%.…”

Section: Related Workmentioning

confidence: 99%

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Gupta¹,

Sharma

Ahmed

et al. 2021

Neural Process Lett

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The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

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“…In the blood work group, we identified efforts to predict COVID-19 infection based on results from blood, urine, or other laboratory tests. In [ 24 ], the authors reported a sensitivity of 0.93 and a specificity of 0.9333, with an SVM classifier trained with a dataset with 18 blood values. Another work [ 25 ], reported a sensitivity of 0.75 and a specificity of 0.49 using a dataset with laboratory findings (features) to train an XGBoost classifier.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach as an Aid for Early COVID-19 Detection

Velazquez

Tobon

Sánchez

et al. 2021

Sensors

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The novel coronavirus SARS-CoV-2 that causes the disease COVID-19 has forced us to go into our homes and limit our physical interactions with others. Economies around the world have come to a halt, with non-essential businesses being forced to close in order to prevent further propagation of the virus. Developing countries are having more difficulties due to their lack of access to diagnostic resources. In this study, we present an approach for detecting COVID-19 infections exclusively on the basis of self-reported symptoms. Such an approach is of great interest because it is relatively inexpensive and easy to deploy at either an individual or population scale. Our best model delivers a sensitivity score of 0.752, a specificity score of 0.609, and an area under the curve for the receiver operating characteristic of 0.728. These are promising results that justify continuing research efforts towards a machine learning test for detecting COVID-19.

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Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment

Cited by 168 publications

References 28 publications

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

A Machine Learning Approach as an Aid for Early COVID-19 Detection

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