X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture

Toni Arifin; Naufal Hidayah Surya

doi:10.32520/stmsi.v11i3.1597

X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture

Toni Arifin, Naufal Hidayah Surya

Abstract

Pneumonia is one of the deadliest diseases, killing 2-3 million people in developed countries like the United States. Based on WHO's view, pneumonia is one of the leading causes of death in children worldwide, WHO says many children under the age of 5 have died from the disease. And in 2017, the World Health Organization (commonly known as WHO) reported that pneumonia had claimed the lives of 800,000 children under the age of 5. This is why researchers developed this program, to help the public diagnose pneumonia. In this study, we generated a Deep Learning model using the CNN (Convolutional neural network) approach using the VGG16 architecture for thoracic pneumonia classification and normalization. The results of this study show that the Convolution neural network method can classify chest X-ray results with pneumonia with the highest accuracy of 0.9772

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DOI: https://doi.org/10.32520/stmsi.v11i3.1597

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