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All Journal BERKALA FISIKA Jurnal Matematika & Sains Journal of Medical Physics and Biophysics Makara Journal of Science Indonesian Journal of Physics (IJP)
Idam Arif
Institut Teknologi Bandung
Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Mathematics Medicine & Pharmacology Physics

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Multiclass Classification of Covid-19 CT Scan Images With VGG-16 Architecture Using Transfer Learning System Tan, Nurlaila; Arif, Idam
Indonesian Journal of Physics Vol 35 No 1 (2024): vol 35 no 1 2024
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itb.ijp.2024.35.1.4

Abstract

COVID-19 is a respiratory disease caused by the coronavirus. The most common test technique used today for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR). However, compared to RT-PCR, radiological imaging such as X-rays and computer tomography (CT) may be a more precise, useful, and faster technology for COVID-19 classification. X-rays are more accessible because they are widely available in all hospitals in the world and are cheaper than CT scans, but the classification of COVID-19 using CT scan images is more sensitive than X-rays. Therefore, CT scan images can be used for the early detection of COVID-19 patients. One of them is using the deep learning method. In this study, a CNN algorithm with a VGG-16 architecture will be selected to classify COVID-19, intermediate, and non-COVID CT scan images using 2481 image datasets. First, pre-processing is done by resizing the image, converting the image channel into RGB, and dividing the dataset into a training dataset and a testing dataset. Then, the convolution process is continued by utilizing the pre-trained VGG-16 model from ImageNet. The results of testing the data with 97% accuracy were obtained. It is concluded that the model used to classify COVID-19, intermediate, and non-COVID CT scan images is effective and produces good results.
Co-Authors Astalini Astalini Bergas Bimo Branarto Choirul Anam AM Diponegoro Delianis Pringgenis Eko Hidayanto Freddy Haryanto Freddy Haryanto Geoff Dougherty Handayani, Nita Nita Handayani Octolia Tobigasa Tambunan Poppy Intan Tjahaja Poppy Intan Tjahaja Pratama, Sra Harke Pringgenis, Delianis Ratnawulan . Rena Widita Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Siti Nurul Khotimah Sparisoma Viridi Sra Harke Pratama Tan, Nurlaila The Houw Liong