Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.

Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.

An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices.

An ECG is a diagnostic technique that examines and records the heart's electrical impulses. It is easy to categorise and prevent computational abstractions in the ECG signal using the conventional method for obtaining ECG features. It is a significant issue, but it is also a difficult and time-consuming chore for cardiologists and medical professionals. The proposed classifier eliminates all of the following limitations. Machine learning in healthcare equipment reduces moral transgressions. This study's primary purpose is to calculate the R-R interval and analyze the blockage utilising simple algorithms and approaches that give high accuracy. The MIT-BIH dataset may be used to rebuild the data. The acquired data may include both normal and abnormal ECGs. A Gabor filter is employed to generate a noiseless signal, and DCT-DOST is used to calculate the signal's amplitude. The amplitude is computed to detect any cardiac anomalies. A genetic algorithm derives the main highlights from the R peak and cycle segment length underlying the ECG signal. So, combining data with specific qualities maximises identification. The genetic algorithm aids in hereditary computations, which aids in multitarget improvement. Finally, Radial Basis Function Neural Network (RBFNN) is presented as an example. An efficient feedforward neural network lowers the number of local minima in the signal. It shows progress in identifying both normal and abnormal ECG signals.