Development of an Effective Deep Learning Model for COVID-19 Detection from CT Images

Abstract

Coronavirus Disease (COVID-19) is an RNA-type virus that is spreading worldwide. COVID-19, which was first seen in Wuhan, China, in December 2019, quickly began to be seen in all countries of the world. Symptoms such as respiratory tract infections, fever, cough and shortness of breath are common in the diagnosis of the disease. The detection of the disease is done in the first stage by applying the Polymerase Chain Reaction (PCR) test. The long duration of laboratory examinations has led researchers to different methods. In this study, a model that can help radiologists detect the disease through Computed Tomography (CT) images was designed. This system, based on deep learning, aims to detect the disease by classification method through COVID-19 positive and negative chest tomography images. The data set used in the study consists of a total of 5000 images. Experimental studies have been conducted on Convolutional Neural Network (CNN) models such as AlexNet, Densenet201, GoogleNet, ResNet-50, Vgg-16, EfficientNet and the proposed CNN model. With the designed CNN model, COVID-19 was predicted with a success rate of 99.20%. An effective and successful model is proposed for COVID-19 detection from CT images.

Keywords

• Deep Learning
• Image Classification
• COVID-19
• Computed Tomography

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