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.
Highlights
In recent years, Internet of ings (IoT) devices are widely used in a large number of applications such as smart cities, manufacturing, home automation, and medicine [1]. ese devices are used to capture information about the physical world through sensors
IoT devices are used for extracting data from COVID-19 patients remotely. is information is transferred to healthcare workers for diagnosis of COVID-19 [2]. ese devices reduce the burden on healthcare workers and recognize the unusual patterns from the extracted sensor information
deep learningbased chest radio classification (DL-CRC) achieved the classification accuracy of 93.94%. e sensitivity, specificity, and accuracy obtained from 3D convolution neural network (3DCNN) were 86.9%, 90.1%, and 87.5%, respectively. e classification accuracy, specificity, and sensitivity achieved by CheXNet were 97.9%, 97.9%, and 98.8%, respectively
Summary
Internet of ings (IoT) devices are widely used in a large number of applications such as smart cities, manufacturing, home automation, and medicine [1]. ese devices are used to capture information about the physical world through sensors. IoT devices are used for extracting data from COVID-19 patients remotely. Is information is transferred to healthcare workers for diagnosis of COVID-19 [2]. Ese devices reduce the burden on healthcare workers and recognize the unusual patterns from the extracted sensor information. Healthcare workers provide better treatment for coronavirus-infected persons promptly using IoT-enabled devices. Ere is a need to develop an automatic classification technique by using the information provided by IoT devices. Journal of Healthcare Engineering (+), pneumonia, tuberculosis, or healthy It is preferred over other imaging techniques due to cost-effectiveness and having lower risk of radiation exposure to humans. E deep ensemble model helps the radiologist to timely diagnose the infected patients
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