Detection of COVID-19 Infection Using Convolutional Neural Network
Lecture Notes on Data Engineering and Communications Technologies
; 142:363-372, 2023.
Article
in English
| Scopus | ID: covidwho-2238743
ABSTRACT
Coronavirus disease (COVID-19) is a newly discovered viral sickness that can be fatal. The majority of patients will experience mild to severe respiratory problems and will improve without need for special treatment. Persons over 65, and for those who are underlying medical disorders such cardiovascular disease, asthma, respiratory illness, and cancer, are more prone for developing severe symptoms. In these conditions, 3D volumetric imaging has proven to be a useful technique for COVID-19 patient diagnosis and prognosis. We present a new approach for detecting and classifying COVID-19 infection using 3D volumetric lung imaging in this work. For the detection and classification process, we have used 3D volumetric image processing and deep learning techniques, respectively. Early recognition and finding are basic elements to stop COVID-19 spreading. Various profound learning-based approaches had been proposed for COVID-19 separating CT examines as an instrument to computerize and assist with finding. These methods suffer with at least one of the faults listed below (i) They treat each CT scan individually (ii) These methods are trained and tested on the same dataset. To address these two challenges, we present an accurate deep learning technique for COVID-19 screening using a democratic framework in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Computerized tomography; Convolutional neural networks; Deep learning; Diagnosis; Image classification; Learning algorithms; Learning systems; Patient treatment; 3d volumetric image processing; Convolutional neural network; Coronavirus disease (COVID-19); Coronaviruses; Deep learning technique; Detection; Images processing; Learning techniques; Respiratory problems; Volumetric images; Coronavirus; COVID-19; Classification; Deep learning techniques
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Lecture Notes on Data Engineering and Communications Technologies
Year:
2023
Document Type:
Article
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