Methodologies, Applications, and Challenges of Pneumonia Detection of Chest X-Ray images for COVID-19 using IoT-enabled Deep Learning

Abstract

There is a great advancement in the domain of Internet of medical Things (IoMT) including other domains of artificial intelligence, machine learning, deep learning which has an extensive possibility of exploring healthcare industry. The IoT devices like sensors, actuators and other devices gets connected to the internet and further they collect the data and store it to a specific location. For further processing of the data, the frameworks of machine learning, deep learning are utilized. These techniques help to get the clear insights of the patient’s health data which enables to know the current health status of the patient. Recently, the Covid-19 outbreak has occurred which has influenced millions of people across the globe. This virus has taken life of many people and the infection rate of this virus is still increasing day by day. Researchers and medical staffs are exploring advanced techniques to utilize medical images of the infected person using IoMT and deep learning frameworks so that the root cause can be explored. Different techniques deep neural networks have been explored in this work to detect Covid-19 infected persons which utilizes a chest X-ray dataset. A lot of challenges are there that are being faced by the researchers to detect Covid-19 infected patients from Chest X-ray images. This exhaustive literature review presents different frameworks of deep learning architectures and a comparative study has also been done addressing the recent methodologies, datasets, issues, research gaps and so on. Further, some pre-trained models based on CNN architectures like Xception, VGG16, VGG19 and so on are also discussed.