Identification of Pneumonia on Thorax X-Ray Image Using the Convolutional Neural Network Method Model VGG16

Abstract

Pneumonia is an inflammation of the lung parenkime caused by bacteria, viruses, and other infections. Generally, the detection of pneumonia can be done by analyzing the x-ray image of the thorax after the reported symptoms and recommendations given by the physician. Previous studies said that Convolutional Neural Networks (CNN), part of Deep learning technology, can be used to analyze x-ray images. It also reported that this method could reduce independent parameters and handle the deformation of input images, such as translation, rotation, and scale. In this study, we report the implementation of CNN model VGG16 with varying epochs and pixels on chest x-ray images to classify pneumonia. The data used are 4000 x-ray images of the thorax taken from the Mendeley website. Final classification processes were done by using the softmax activation function. The results were tested using 20 batch sizes based on 2 image treatment parameters, namely resize and epoch. We reported that the higher image reduction size can increase the average calculation’s accuracy. It is found that the highest accuracy (87,54%) is obtained from the resizing of 300×300 pixels. The lowest average accuracy, 79.61%, is shown at a resize size of 100×100 pixels. The highest accuracy (94.39%) for the epoch variant on the resized image of 300×300 pixels is obtained on the 30th epoch