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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Muhammad Hafidh Firmansyah
Kyungpook National University, Republic of Korea
Computer Science & IT Electrical & Electronics Engineering

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Light-weight MobileNet for Fast Detection of COVID-19 Muhammad Hafidh Firmansyah; Seok-Joo Koh; Wahyu Kurnia Dewanto; Trismayanti Dwi Puspitasari
Jurnal Teknologi Informasi dan Terapan Vol 8 No 1 (2021)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v8i1.214

Abstract

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.
Co-Authors Puspitasari, Trismayanti Dwi Seok-Joo Koh Wahyu Kurnia Dewanto