Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients

Joint Authors

Fan, Zeming
Jamil, Mudasir
Sadiq, Muhammad Tariq
Huang, Xiwei
Yu, Xiaojun

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-24

Country of Publication

Egypt

No. of Pages

13

Main Topic

Public Health
Medicine

Abstract EN

Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease.

Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose.

However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications.

In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data.

Transfer learning builds accurate models in a timesaving way.

First, data augmentation was performed to help the network for memorization of image details.

Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting.

All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method.

The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations.

The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients.

American Psychological Association (APA)

Fan, Zeming& Jamil, Mudasir& Sadiq, Muhammad Tariq& Huang, Xiwei& Yu, Xiaojun. 2020. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186643

Modern Language Association (MLA)

Fan, Zeming…[et al.]. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1186643

American Medical Association (AMA)

Fan, Zeming& Jamil, Mudasir& Sadiq, Muhammad Tariq& Huang, Xiwei& Yu, Xiaojun. Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186643

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186643