Deep Learning-based Decision Support System for classification of COVID-19 and Pneumonia patients

Abstract

The fast spread of Coronavirus (COVID-19) poses a huge risk to people all around the world. Recently, COVID-19 testing kits have been unavailable due to rise in effected people and large demand of tests. Keeping the urgency of the situation in mind, an automatic diagnosis method for early detection of COVID-19 is needed. The proposed deep learning decision support system (DSS) for COVID-19 employs MobileNet v2 Deep learning (DL) model for effective and accurate detection. Here we collected Cough auscultations through self-designed digital sensor. The primary experimental results show that the maximum accuracy for training is around 99.91%, and the maximum accuracy for validation is 98.61%, with 97.5% precision, 98.5%recall, and 98% F1-score. The Deep Learning-based model described here strives for similar performance to medical professionals and can help pulmonologist/radiologists increase their working productivity.

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

“…A deep learning model is usually a three-or more-layered neural network and is a type of AI [31,32]. These neural networks attempt, to mimic the functioning of the human brain by "learning" from vast amounts of data as done in [33,34].…”

“…[31] applied FDEP to FallCNN_1, FallCNN_2, Fall_CNN_3, and TLD. [32] FDS using accelerometer-gyro sensors. Thousands of images are taken and played at high speed to make films.…”