Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices

Yogesh H. Bhosale, Shrinivas R. Zanwar, Zakee Ahmed, Mahendra Nakrani, Devendra Bhuyar, Ulhas B. Shinde

20222022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)29 citationsDOI

Abstract

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub.

Topics & Concepts

Convolutional neural networkDeep learningComputer scienceArtificial intelligenceCoronavirus disease 2019 (COVID-19)Artificial neural networkPneumoniaContextual image classificationMulticlass classificationMachine learningPattern recognition (psychology)DiseaseMedicineImage (mathematics)PathologyInfectious disease (medical specialty)Support vector machineInternal medicineCOVID-19 diagnosis using AIAI in cancer detectionRadiomics and Machine Learning in Medical Imaging