Development of Smart Healthcare Monitoring System in IoT Environment

Islam, Md. Milon; Rahaman, Ashikur

doi:10.1007/s42979-020-00195-y

Cited by 409 publications

(167 citation statements)

References 32 publications

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“…Many researchers have demonstrated various approaches to detect COVID-19 utilizing X-ray images. Recently, computer vision [ 10 ], machine learning [ [11] , [12] , [13] ], and deep learning [ 14 , 15 ] have been used to automatically diagnose several diverse ailments in the human body, which ensures smart healthcare [ 16 , 17 ]. The deep learning method is used as a feature extractor that enhances classification accuracies [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

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525

278

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

Self Cite

525

278

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“…As huge dataset generated around the world related to 2019-nCoV pandemic everyday is a treasured resource to be mined and analyzed for useful, valid, and novel knowledge or patterns extraction for better decision-making to contain the outbreak of COVID-19 pandemic. In healthcare sector, data mining has been widely applied in many different applications such as predicting patient outcomes, modeling health outcomes, hospital ranking, and evaluation of treatment effectiveness and infection control, stability, and recovery [1,23,29].…”

Section: Introductionmentioning

confidence: 99%

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

et al. 2020

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Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

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“…caused severe and acute actions on human health, and artificial intelligence-based systems show better performance to identify those diseases. Fighting against COVID-19, modern technologies are playing significant roles in the development of a smart healthcare system [ 14 , 15 ]. For example, a facial recognition system is used to trace the infected patients, and robots are used to deliver food and medicine in hospital and drones are applied to disinfect streets [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

2020

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During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.

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Development of Smart Healthcare Monitoring System in IoT Environment

Cited by 409 publications

References 32 publications

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic

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