Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases.
Ahmed I M IskanderaniIbrahim M MehediAbdulah Jeza AljohaniMohammad ShorfuzzamanFarzana AktherThangam PalaniswamyShaikh Abdul LatifAbdul LatifAftab AlamPublished in: Journal of healthcare engineering (2021)
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.
Keyphrases
- sars cov
- artificial intelligence
- coronavirus disease
- deep learning
- end stage renal disease
- pulmonary embolism
- newly diagnosed
- healthcare
- ejection fraction
- chronic kidney disease
- convolutional neural network
- high resolution
- prognostic factors
- peritoneal dialysis
- respiratory syndrome coronavirus
- big data
- computed tomography
- high throughput
- patient reported outcomes
- mass spectrometry
- combination therapy
- magnetic resonance
- room temperature
- replacement therapy
- patient reported