This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19 cases. The framework utilizes a multi-access edge computing technology such that end-user can access available resources as well the CNN on the cloud. Early detection of COVID-19 can improve treatment and mitigate transmission. During peaks of infection, hospitals worldwide have suffered from heavy patient loads, bed shortages, inadequate testing kits and short-staffing problems. Due to the timeconsuming nature of the standard RT-PCR test, the lack of expert radiologists, and evaluation issues relating to poor quality images, patients with severe conditions are sometimes unable to receive timely treatment. It is thus recommended to incorporate computational intelligence methodologies, which provides highly accurate detection in a matter of minutes, alongside traditional testing as an emergency measure. CNN has achieved extraordinary performance in numerous computational intelligence tasks. However, finding a systematic, automatic and optimal set of hyperparameters for building an efficient CNN for complex tasks remains challenging. Moreover, due to advancement of technology, data are collected at sparse location and hence accumulation of data from such a diverse sparse location poses a challenge. In this article, we propose a framework of computational intelligence-based algorithm that utilize the recent 5G mobile technology of multi-access edge computing along with a new CNN-model for automatic COVID-19 detection using raw chest X-ray images. This algorithm suggests that anyone having a 5G device (e.g., 5G mobile phone) should be able to use the CNN-based automatic COVID-19 detection tool. As part of the proposed automated model, the model introduces a novel CNN structure with the genetic algorithm (GA) for hyperparameter tuning. One such combination of GA and CNN is new in the application of COVID-19 detection/classification. The experimental results show that the developed framework could classify COVID-19Extended author information available on the last page of the article X-ray images with 98.48% accuracy which is higher than any of the performances achieved by other studies.
Keywords Classification • CNN • COVID-19 • Genetic Algorithm • Multi-access edge 1 IntroductionAn automatic detection of Coronavirus disease 2019 (COVID-19) can be developed through using the modern computational intelligence techniques and resources available on the high-performance computing facilities, e.g., cloud. The advent of convolutional neural network (CNN), a variant computational intelligence (CI) technique, has made the task of feature extraction from images and image analysis efficient. Moreover, the availability of high-performance computing (HPC) facilities, e.g., distributed edges on cloud, can help us to access COVID-19 data scattered at distant locations Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease. It is caused by severe acute re...