Light-weight MobileNet for Fast Detection of COVID-19
The machine learning models based on Convolutional Neural Networks (CNNs) can be effectively used for detection and recognition of objects, such as Corona Virus Disease 19 (COVID-19). In particular, the MobileNet and Single Shot multi-box Detector (SSD) have recently been proposed as the machine learning model for object detection. However, there are still some challenges for deployment of such architectures on the embedded devices, due to the limited computational power. Another problem is that the accuracy of the associated machine learning model may be decreased, depending on the number of concerned parameters and layers. This paper proposes a light-weight MobileNet (LMN) architecture that can be used to improve the accuracy of the machine learning model, with a small number of layers and lower computation time, compared to the existing models. By experimentation, we show that the proposed LMN model can be effectively used for detection of COVID-19 virus. The proposed LMN can achieve the accuracy of 98% with the file size of 27.8 Mbits by replacing the standard CNN layers with separable convolutional layers.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Jurnal Teknologi Informasi dan Terapan (J-TIT) and Department of Information Technology, Politeknik Negeri Jember as publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. Authors should sign a copyright transfer agreement when they have approved the final proofs sent by Jurnal Teknologi Informasi dan Terapan (J-TIT) prior to the publication. The copyright transfer agreement can be download here .
Jurnal Teknologi Informasi dan Terapan (J-TIT) and Department of Information Technology, Politeknik Negeri Jember and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Teknologi Informasi dan Terapan (J-TIT) are the sole responsibility of their respective authors and advertisers.
Users of this website will be licensed to use materials from this website following the Creative Commons Attribution 4.0 International License. No fees charged. Please use the materials accordingly.
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.