After the World War II, every country throughout the world is experiencing the biggest crisis induced by the devastating Coronavirus disease (COVID-19), which initially arose in the city of Wuhan in December 2019. This global pandemic has severely affected not only the health of billions of people but also the economy of countries all over the world. It has been evident that novel virus has infected a total of 20,674,903 lives as on 12 August 2020. The dissemination of the virus can be regulated by detecting the positive COVID cases as soon as possible. The reverse-transcriptase polymerase chain reaction (RT-PCR) is the basic approach used in the identification of the COVID-19. As RT-PCR is less sensitive to determine the novel virus at the beginning stage, it is worthwhile to develop more robust and other diagnosis approaches for the detection of the novel coronavirus. Due to the accessibility of medical datasets comprising of radiography images publicly, more robust diagnosis approaches are contributed by the researchers and technocrats for the identification of COVID-19 images using the techniques of deep leaning. In this paper, we proposed VGG16 and MobileNet-V2, which makes use of ADAM and RMSprop optimizers for the automatic identification of the COVID-19 images from other pneumonia chest X-ray images. Then, the efficiency of the proposed methodology has been enhanced by the application of data augmentation and transfer learning approach which is used to overcome the overfitting problem. From the experimental outcomes, it can be deduced that the proposed MobileNet-V2 model using ADAM and RMSprop optimizer achieves better accomplishment in terms of accuracy, sensitivity and specificity when contrasted with the VGG 16 using ADAM and RMSprop optimizers.

In this paper, we propose an ensemble-based transfer learning method to predict the X-ray image of a COVID-19 affected person. We have used a weighted Euclidean distance average as the parameter to ensemble the transfer learning model viz. ResNet50, VGG16, VGG19, Xception, and InceptionV3. Image augmentations have been carried out using generative adversarial network modelling. We took 784 training images, and 278 test images to validate our model accuracy, and the accuracy of our proposed model was around 98.67% for the training data set and 95.52% for the test data set. Along with that, we also propose a genetic algorithm optimized classification algorithm, to analyze the symptoms of COVID-19 for low, medium, and high-risk patients. The accuracy for the optimized set overshadowed the accuracy of un-optimized classification, and the optimized accuracy is as high as 88.96% for the optimized model. The novelty of this paper lies in the bi-sided model of the paper, i.e., we propose two major models, and one is the genetic algorithm optimized model to analyze the symptoms for a patient of varied risk and the other is to classify the X-ray image using an ensemble-based transfer learning model.

The rise of digital technology has essentially enhanced the overall communication and data management system, facilitating essential medical care services. Considering this aspect, the healthcare system successfully managed patient requirements through online services and facilitated patient experience. However, the lack of adequate data security and increased digital activities during Covid-19 made the healthcare system a soft target for hackers to gain unauthorized access and steal crucial and sensitive information. Countries such as the UK and the US recently received such challenges, highlighting the need for effective data maintenance. IoT emerged as one of the critical solutions for data management systems in terms of addressing data security which certainly can enhance overall data collection, storage, maintenance, prediction of potential data security breaches and taking appropriate measurements. The concerned research considers a secondary data collection process where necessary data is collected from original scholarly articles, books and journals. Apart from that, a positivism research philosophy, a deductive research approach and a descriptive research design have been considered for this study. Qualitative data analysis techniques have also been incorporated into this research. Upon viewing the pros and cons of IoT algorithms, DES, AES, triple data encryption standards, and RSA encryption can be used in the healthcare system to facilitate data protection.

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