Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Iskanderani, Ahmed I.; Mehedi, Ibrahim M.; Aljohani, Abdulah Jeza; Akther, Farzana; Palaniswamy, Thangam; Latif, Shaikh Abdul; Latif, Abdul; Alam, Aftab

doi:10.1155/2021/3277988

Cited by 24 publications

(21 citation statements)

References 20 publications

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“…( 2020 ) ResNet, SqueezeNet, and DenseNet121 98.00 Iskanderani et al. ( 2021 ) DenseNet 96.25 Pathak et al. ( 2020 ) ResNet32 96.22 Apostolopoulos and Mpesiana ( 2020 ) MobileNetV2 96.78 Kaur et al.…”

Section: Discussionmentioning

confidence: 99%

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RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

Kaya

Gürsoy

2023

Soft Comput

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COVID-19 is a virus that causes upper respiratory tract and lung infections. The number of cases and deaths increased daily during the pandemic. Once it is vital to diagnose such a disease in a timely manner, the researchers have focused on computer-aided diagnosis systems. Chest X-rays have helped monitor various lung diseases consisting COVID-19. In this study, we proposed a deep transfer learning approach with novel fine-tuning mechanisms to classify COVID-19 from chest X-ray images. We presented one classical and two new fine-tuning mechanisms to increase the model’s performance. Two publicly available databases were combined and used for the study, which included 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. The models achieved average accuracy rates of 95.62%, 96.10%, and 97.61%, respectively, for 3-class cases with fivefold cross-validation. Numerical results show that the third model reduced 81.92% of the total fine-tuning operations and achieved better results. The proposed approach is quite efficient compared with other state-of-the-art methods of detecting COVID-19.

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Section: Discussionmentioning

confidence: 99%

“…These studies include different CNN models such as Resnet50, VGG16 (Shorfuzzaman and Masud 2020 ), MobileNetV2 (Apostolopoulos and Mpesiana 2020 ), DenseNet (Iskanderani et al. 2021 ), and several customized new CNN models such as DarkCovidNet (Ozturk et al. 2020 ) , InstaCovidNet (Gupta et al.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

Kaya

Gürsoy

2023

Soft Comput

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“…Further, we also analyzed the systems which can categorize three or more classes. It is observed from Table 2 that ResNet50 with DWT and GLCM [ 114 ], customized CNN [ 118 , 154 , 179 , 189 ], GoogLeNet [ 138 ], InceptionNet [ 141 ], AlexNet [ 160 ], a combination of DenseNet103 and ResNet18 [ 148 ], an ensemble of various models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201 [ 153 ], and a grouping of MobileNet and InceptionV3 [ 161 ] were effectively used for four-class classification using X-ray images. Further, the authors also used CNN models for five-class classification using X-ray images [ 129 , 168 , 177 ].…”

Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in COVID-19 Detection

Gudigar

Raghavendra

Nayak

et al. 2021

Sensors

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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

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“…It was observed that the collection of pre-trained models provided more efficient results than individual models. 77 …”

Section: Applications Of Iomt In Healthcarementioning

confidence: 99%

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Dwivedi

Mehrotra

Chandra

2022

Journal of Oral Biology and Craniofacial Research

169

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Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

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Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Cited by 24 publications

References 20 publications

RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

Role of Artificial Intelligence in COVID-19 Detection

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

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