Performances Analysis of Convolutional Neural Network VGG16 Model on Covid-19 Classification Using Radiographic and CT-Scan Images

Abstract

Corona Virus Disease 2019 (COVID-19) infection is an infectious disease that is a public health concern globally. Diagnosis through medical images can identify COVID-19 cases to combat the virus. However, as the number of COVID-19 cases continues to increase, the time available to review cases is limited. This can lead to heavy workload and high stress levels for radiologists, which in turn can lead to errors in analyzing images (missed findings). Therefore, automatic detection of COVID-19 infection based on deep learning is needed to analyze medical images such as radiographs and CT-Scans quickly and efficiently. This study aims to analyze the performance of the VGG16 model in performing identification between COVID-19 and Normal on radiographic and CT-Scan images. To find the optimal model for the classification task, the VGG16 model was tested with two approaches: without and with transfer learning (TL). A total of 800 images were used in this study and divided into 3 parts, 80% for training, 10% for validation, and 10% for testing. The results showed that the VGG16 model with TL achieved the highest accuracy value, which was 93.7% on radiographic images and 73.3% on CT-Scan images. This information indicates that the use of transfer learning method with VGG16 model proposed in this study is effective in recognizing binary class cases using radiographic and CT-Scan images. Thus, the application of this model can provide significant benefits and contributions in disease diagnosed, help reduce radiologist workload, and improve patient handling.