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

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

Classification, Convolutional Neural Network, COVID, Machine Learning, Image

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%

Melly Damara Chaniago, Amellia Amanullah Sugiharto, Qhistina Dyah Khatulistiwa, Zamah Sari, Agus Eko

Universitas Muhammadiyah Malang

20-04-2022

Fajar bagus W

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Indonesia

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