Light-weight MobileNet for Fast Detection of COVID-19

Firmansyah, Muhammad Hafidh and Koh, Seok-Joo and Dewanto, Wahyu Kurnia and Puspitasari, Trismayanti Dwi (2021) Light-weight MobileNet for Fast Detection of COVID-19. Jurnal Teknologi Informasi dan Terapan (J-TIT), 8 (1). pp. 65-71. ISSN 2354-838X

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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, comparedto 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 layerswith separable convolutional layers

Item Type: Article
AuthorFirmansyah, Muhammad HafidhUNSPECIFIED
AuthorKoh, Seok-JooUNSPECIFIED
AuthorDewanto, Wahyu Kurnia0008047103
AuthorPuspitasari, Trismayanti Dwi0027029002
Uncontrolled Keywords: MobileNet; light-weight; SSD; CNN; COVID-19;
Subjects: 410 - Rumpun Ilmu Teknik > 450 - Teknik Elektro dan Informatika > 458 - Teknik Informatika
Divisions: Jurusan Teknologi Informasi > Prodi D4 Teknik Informatika > Publikasi
Depositing User: Trismayanti Dwi Puspitasari
Date Deposited: 12 Jul 2022 04:43
Last Modified: 12 Jul 2022 04:43

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