IoT Deployable Lightweight Deep Learning Application For COVID-19 Detection With Lung Diseases Using RaspberryPi

ABSTRACT

Proper assessment of COVID-19 patients has become critical to mitigating and halting the disease's rapid expansion during the present COVID-19 epidemic across the nations. Due to the presence of chronic lung/pulmonary diseases, the intensity and demise rates of COVID-19 patients were increased. This study will analyze radiography utilizing chest X-ray images (CXI), one of the most successful testing methods for COVID-19 case identification. Given that deep learning (DL) is a useful method and technique for image processing, there have been several research on COVID-19 case identification using CXI to train DL models. While few of the study claims outstanding predictive outcomes, their suggested models may struggle with overfitting, excessive variance, and generalization mistakes due to noise, a limited number of datasets and could not be deployed to IoT devices due to heavy network size. Considering deep Convolutional Neural Network (CNN) can conquer the weaknesses by getting predictions with several diseases using a single model deployed on a real-time IoT device. We propose a lightweight Deep Learning model (LDC-Net) that has spearheaded an open-sourced COVID-19 case identification technique using CNN-generated CXI by utilizing a suggested strategy aware of distinct features learning of different classes. Experimental results on Raspberry Pi show that LDC-Net provides encouraging outputs for detecting COVID-19 cases with an overall 96.86% precision, 96.78% recall, 96.77% F1-score, and 99.28% accuracy, better than other state-of-the-art models. By empowering the Internet of Things-IoT and IoMT devices, this suggested framework can identify COVID-19 from CXI and other seven lung diseases with healthy labels. © 2022 IEEE.