Discerning COVID-19 from mycoplasma and viral pneumonia on CT images via deep learning

Zaman²,

et al. 2021

Electronics

Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

Section: Machine Learning Technologies To Mitigate Covid-19 Challengesmentioning

confidence: 99%

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Zaman²,

et al. 2021

Electronics

Anais Do XIV Encontro Unificado De Computação Do Piauí (ENUCOMPI 2021)

“…Os autores em [Yener and Oktay 2020] trazem um estudo sobre três modelos populares de CNN usando aprendizagem por transferência, onde a VGG16 e VGG19 obtiveram resultados semelhantes e melhores que a Xception. Os autores [Serte and Serener 2020] tiveram como objetivo distinguir COVID-19 de outras infecc ¸ões. Dentre as CNNs utilizadas, a ResNet-50 se destacou, para todos casos ela obteve os melhores resultados.…”

Section: Detecc ¸ãO Da Covid-19: Tomografias Computadorizadasunclassified

A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning

Marques¹,

Veras²,

Silva³

2021

Through the development of the COVID-19 disease, various diagnosis methods have been studied. One of them is the computed tomography (CT), which has the best level of detail among medical image exams. The CT generates a repeatable and tiring workload, in addition to needing a team that is familiar with the findings that indicate pneumonia caused by COVID-19. To reduce this manual work and collaborate with these teams, several studies have been carried out using deep learning techniques. In this way, this study presents a review of the literature regarding the detection of COVID-19 in CT that uses deep learning to collaborate with a theoretical basis for future works.

Convolutional neural networks for the classification of chest X-rays in the IoT era

Almezhghwi

Serte²,

Al‐Turjman³

2021

Multimed Tools Appl

Self Cite

Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is the most accurate way of detecting most thorax diseases. Radiologists examine these images to identify lung diseases; however, this process can require some time. In contrast, an automated artificial intelligence system could help radiologists detect lung diseases more accurately and faster. Therefore, we propose two artificial intelligence approaches for processing and identifying chest X-ray images to detect chest diseases from such images. We introduce two novel deep learning methods for fast and automated classification of chest X-ray images. First, we propose the use of support vector machines based on the AlexNet model. Second, we develop support vector machines based on the VGGNet16 method. Combined deep networks with a robust classifier have shown that the proposed methods outperform AlexNet and VGG16 deep learning approaches for the chest X-ray image classification tasks. The proposed AlexNet and VGGNet based SVM provide average area under the curve values of 98 % and 97 % , respectively, for twelve chest X-ray diseases.