Internet of Medical Things (IoMT) with machine learning–based COVID-19 diagnosis model using chest X-ray images

The outbreak of COVID-19 in Wuhan, China severely affected other parts of the world at a drastic rate. COVID-19 is classically diagnosed by a reverse-transcription polymerase chain reaction test on a blood sample. However, it has some limitations related to the sensitivity and availability of tests and the turnaround times for results. To resolve these issues, artificial intelligence techniques can diagnose COVID-19 from computed tomography scans and investigate radiological features for accurate COVID-19 diagnosis. This chapter presents a new Internet of Medical Things–based COVID-19 diagnosis model using different machine learning–based classification models on chest X-rays. The proposed model initially collects the samples of patients using Internet of Things devices and transfer the data to the cloud server, where actual diagnosis takes place. Once diagnosis is completed, the report is transferred to the concerned health care centers for further processing. For purposes of diagnosis, a series of processes involves preprocessing, texture feature extraction, and classification. The performance of the proposed model has been validated using a chest X-ray dataset. Analysis of the experimental results indicated that the AdaBoost with random forest model is superior to other models with a maximum accuracy of 90.13%, F score of 90.28%, kappa value of 89.59%, and Mathew Correlation Coefficient (MCC) of 87.44%. The attained results demonstrated that the proposed model is effective for the diagnosis of COVID-19 along with severe acute respiratory syndrome over comparable methods.