Early diagnosis of COVID-19 patients using deep learning-based deep forest model

Title

Early diagnosis of COVID-19 patients using deep learning-based deep forest model

Creator

Singh D.; Kumar V.; Kaur M.; Kumari R.

Description

Coronavirus disease-19 (COVID-19) has rapidly spread all over the world. It is found that the low sensitivity of reverse transcription-polymerase chain reaction (RT-PCR) examinations during the early stage of COVID-19 disease. Thus, efficient models are desirable for early-stage testing of COVID-19 infected patients. Chest X-ray (CXR) images of COVID-19 infected patients have shown some bilateral changes. In this paper, deep transfer learning and a deep forest-based model are proposed to diagnose COVID-19 infection from CXR images. Initially, features of X-ray images are extracted using the well-known deep transfer learning model (i.e., ResNet101), which does not require tuning many parameters compared to the deep convolutional neural network (CNN). After that, the deep forest model is utilised to predict COVID-19 infected patients. The deep forest is based upon ensemble learning and requires a small number of hyper-parameters. Additionally, the proposed model is trained on a multi-class dataset that contains four different classes as COVID-19 (+), pneumonia, tuberculosis, and healthy patients. The comparisons are drawn among the proposed deep transfer learning and deep forest-based models, the competitive models. The obtained results show that the proposed model effectively diagnoses COVID-19 infection with an accuracy of 99.4%. 2022 Informa UK Limited, trading as Taylor & Francis Group.

Source

Journal of Experimental and Theoretical Artificial Intelligence, Vol-35, No. 3, pp. 365-375.

Date

2023-01-01

Publisher

Taylor and Francis Ltd.

Subject

CNN; COVID-19; deep forest; deep learning; ResNet101; testing

Coverage

Singh D., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea; Kumar V., Department of Computer Science & Engineering, National Institute of Technology Hamirpur, Hamirpur, India; Kaur M., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea; Kumari R., Department of Computer Science, Christ (Deemed to Be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 0952813X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1080/0952813X.2021.2021300

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