Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning
DOI:
https://doi.org/10.30812/matrik.v23i1.3197Keywords:
Chest X-Ray, Convolution Neural Network, Deep Learning, Image Classification, PneumoniaAbstract
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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Indonesia : An Ecological Study,†Journal of Public Health Research and Community Health Development, vol. 6, no. 2, pp.
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Using Keras Module TensorFlow,†Indonesian Applied Physics Letters, vol. 4, no. 1, pp. 38–44, aug 2023.
[4] I. G. A. A. D. Indradewi, N. W. S. Saraswati, and N. W. Wardani, “COVID-19 Chest X-Ray Detection Performance Through
Variations of Wavelets Basis Function,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21,
no. 1, pp. 31–42, nov 2021.
[5] R. a. Rizal, I. S. Girsang, and S. A. Prasetiyo, “Klasifikasi Wajah Menggunakan Support Vector Machine (SVM),†REMIK:
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Vector Machines,†International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 116–121, dec 2020.
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Informasi dan Ilmu Komputer, vol. 8, no. 2, pp. 311–318, mar 2021.
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Images,†Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 8, no. 1, pp. 1–7, mar 2022.
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Food Science and Food Safety, vol. 18, no. 6, pp. 1793–1811, nov 2019.
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Neural Network,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 241–252, nov
2022.
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dengan Arsitektur Efficientnet-B3,†JUST IT : Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, vol. 12, no. 3, pp.
55–59, aug 2022.
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Deep Learning yang Efisien,†Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 6, pp. 1289–1296, dec 2020.
[14] A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross
Validation,†Journal of Applied Informatics and Computing, vol. 4, no. 1, pp. 45–51, may 2020.
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Convolutional Neural Network (DW-CNN),†EAI Endorsed Transactions on Pervasive Health and Technology, vol. â€6â€, no. 23,
pp. 1–10, sep 2020.
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[17] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “COVID-19 Image Data Collection: Prospective
Predictions Are the Future,†pp. 1–38, 2020.
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[19] H. Darmanto, “Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network,†Joined
Journal (Journal of Informatics Education), vol. 2, no. 1, pp. 41–59, jul 2019.
[20] A. R. Syulistyo, D. S. Hormansyah, and P. Y. Saputra, “SIBI (Sistem Isyarat Bahasa Indonesia) Translation using Convolutional
Neural Network (CNN),†IOP Conference Series: Materials Science and Engineering, vol. 732, no. 1, p. 012082, jan 2020.
[21] M. R. R. Allaam and A. T. Wibowo, “Klasifikasi Genus Tanaman Anggrek Menggunakan Metode Convolutional Neural Network
(CNN),†eProceedings of Engineering, vol. 8, no. 2, apr 2021.
[22] R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi Na¨ıve Bayes Classifier (NBC) pada Klasifikasi Status Gizi Balita
Stunting dengan Pengujian K-Fold Cross Validation,†Jurnal Gaussian, vol. 11, no. 1, pp. 130–139, may 2022.
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Size to Implement Transfer Learning for Brain Tumor Classification,†Electronics 2023, Vol. 12, Page 964, vol. 12, no. 4, p.
964, feb 2023.
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2023-10-10
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[1]
N. W. S. Saraswati, I. W. D. Suryawan, N. K. T. Juniartini, I. D. M. K. Muku, P. Pirozmand, and W. Song, “Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning”, MATRIK, vol. 23, no. 1, pp. 17–28, Oct. 2023, doi: 10.30812/matrik.v23i1.3197.
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