Performance Analysis of CT-Scan Covid-19 Classification Using VGG16-SVM - Buana, Rifqi Genta; Abdulloh, Ferian Fauzi (AI Society & STMIK Indonesia, 2023)
The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images. The world was shaken by the emergence of a deadly virus variant called Severe Acute Respiratory Distress Syndrome CoronaVirus 2 which causes COVID-19 disease. This phenomenon started at the end of 2019 which later became an outbreak that caused a deadly pandemic. A significant number of people lose their lives because of this outbreak. A fast and precise diagnosis is needed so that the patients can be treated immediately. This study is intended to overcome these problems by utilizing machine learning to classify lung CT-Scan images. This study propose to use the Convolutional Neural Network (CNN) based on Visual Geometry Group (VGG) 16 layers architecture and Support Vector Machine (SVM) as its classifier. The classification results of the proposed method achieve 89% and 96% accuracy on the two different datasets. This study results can help overcome problems related to the COVID-19 diagnosis and the lack of resources to classify images.