3D CNN-Based Classification of Severity in COVID-19 Using CT Images

Title

3D CNN-Based Classification of Severity in COVID-19 Using CT Images

Creator

Leena Sri R.; Vetriveeran D.; Sambandam R.K.

Description

With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-552, pp. 301-312.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

3D CNN; COVID-19; CT images; Lung infection; Severity classification

Coverage

Leena Sri R., Department of CSE, Thiagarajar College of Engineering, Madurai, India; Vetriveeran D., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Sambandam R.K., Department of CSE, CHRIST (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981196633-0

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-6634-7_22

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149949716&doi=10.1007%2f978-981-19-6634-7_22&partnerID=40&md5=75a7f7d76ed71a4ea30b52a688d014c8