Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases. (28th May 2021)

• Record Type: Journal Article
• Authors: Iskanderani, Ahmed I.; Mehedi, Ibrahim M.; Aljohani, Abdulah Jeza; Shorfuzzaman, Mohammad; Akther, Farzana; Palaniswamy, Thangam; Latif, Shaikh Abdul; Latif, Abdul; Alam, Aftab
• Other Names: Singh Dilbag Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2021(2021)
• Publication Date: 2021-05-28
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2021/3277988
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.