Open AccessLight-weight MobileNet for Fast Detection of COVID-19
Author(s) -
Muhammad Hafidh Firmansyah,
Seok Joo Koh,
Wahyu Dewanto,
Trismayanti Dwi Puspitasari
Publication year - 2021
Publication title -
j-tit : jurnal teknologi informasi dan terapan/j-tit (jurnal teknologi informasi dan terapan)
Language(s) - English
Resource type - Journals
eISSN - 2580-2291
pISSN - 2354-838X
DOI - 10.25047/jtit.v8i1.214
Subject(s) - computer science , convolutional neural network , computation , object detection , artificial intelligence , deep learning , detector , machine learning , covid-19 , algorithm , pattern recognition (psychology) , telecommunications , medicine , disease , pathology , infectious disease (medical specialty)
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.