Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning

Ni Wayan Sumartini Saraswati; I Wayan Dharma Suryawan; Ni Komang Tri Juniartini; I Dewa Made Krishna Muku; Poria Pirozmand; Weizhi Song

doi:10.30812/matrik.v23i1.3197

Ni Wayan Sumartini Saraswati Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
I Wayan Dharma Suryawan Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Ni Komang Tri Juniartini Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
I Dewa Made Krishna Muku Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Poria Pirozmand Holmes Institute, Sydney, Australia
Weizhi Song University of Science and Technology, Hong Kong, China

DOI: https://doi.org/10.30812/matrik.v23i1.3197

Keywords: Chest X-Ray, Convolution Neural Network, Deep Learning, Image Classification, Pneumonia

Abstract

One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases.

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