Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm

Abstract

Corona Virus or also known as COVID-19 is one of the new viruses in 2019. Viruses caused by animal or human disease are called coronaviruses. Coronavirus will direct respiration in humans. Humans who are exposed to the corona virus will experience a respiratory infection. The research that will be made is useful for classifying X-rays of the lungs of patients affected by the coronavirus. In this study, the classification of coronaviruses focuses on three classes, namely Covid, Normal, and Viral Pneumonia. This study uses a lung X-ray image dataset. In this study there are 4 folders in it, namely Scenario 1, Scenario 2, Scenario 3, and Scenario 4. This study will use the Convolutional Neural Network (CNN) method by using an architectural model including Convolutional 2D, activation layers, max pooling layer, dropout layer , flatten, and finally dense layer. After building the model, in each scenario, the results of accuracy, precision, recall, and f1-score will be obtained. The result of accuracy of Scenario 1 is 97.87%, in Scenario 2 the accuracy is 94.84%, in Scenario 3 is 91.66%, and Scenario 4 is 91.41%.