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Implementation of Generative Adversarial Network (GAN) Method for Pneumonia Dataset Augmentation
Corresponding Author(s) : Didih Rizki Chandranegara
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 2, May 2023
Abstract
As a communicable disease, the majority of pneumonia cases are brought on by bacteria or viruses, which cause the lungs' alveoli to swell with fluid or mucus. Pneumonia may arise from this and further making breathing challenging since the lungs' air sacs are unable to contain enough oxygen for the body. Pneumonia may generally be diagnosed clinically (by a physician based on physical symptoms) as well as through a photo chest radiograph, CT scan, and MRI. In this case, the lower cost of a chest radiograph examination making it as one of the most popular medical imaging tests. However, chest radiograph photo readings have a disadvantage, where it takes a long time for medical staff or physicians to identify the patient's illness since it is difficult to detect the condition. Therefore, an identification of chest radiograph imagery into various forms using machine learning becomes one way to address this issue. This research focuses on building a deep neural network model using techniques from the Generative Adversarial Network algorithm. GAN is a category of machine learning techniques using two models to be trained simultaneously, one is a generator model to generated fake data and the other is a discriminator model used to separate the raw data from the real data set images. The dataset used is Chest X-Ray images obtained from repo GitHub and repo Kaggle totaling 5,863 with normal data 1583 images and pneumonia data 4273 imagesThe results showed that the use of the Generative Adevrsarial Network method as augmentation data proved to be more effective in improving the generalization of neural networks, this can be seen from the results the result of the accuracy value obtained is 97%.
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- T. Gabruseva, D. Poplavskiy, and A. Kalinin, “Deep learning for automatic pneumonia detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, pp. 1436–1443, 2020. https://doi.org/10.1109/CVPRW50498.2020.00183
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- A. Sari, “Asuhan Keperawatan pada Anak: Infant (0-12 Bulan) dengan Bronkopneumonia dengan Masalah Keperawatan Ketidakefektifan Bersihan Jalan Napas di Ruang Melati RSUD Ciamis Tahun 2018,” Sekolah Tinggi Kesehatan Bhakti Kencana Bandung, 2018.
- Y. Chandola, J. Virmani, H. S. Bhadauria, and P. Kumar, Deep Learning for Chest Radiographs: Computer-Aided Classification. Elsevier, 2021.
- B. A. Fikri, “Analisis Faktor Risiko Pemberian Asi Dan Ventilasi Kamar Terhadap Kejadian Pneumonia Balita,” The Indonesian Journal of Public Health, vol. 11, no. 1, p. 14, 2016, doi: 10.20473/ijph.v11i1.2016.14-27.
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- M. Mehra, S. Saxena, S. Sankaranarayanan, R. J. Tom, and M. Veeramanikandan, “IoT based hydroponics system using Deep Neural Networks,” Comput Electron Agric, vol. 155, pp. 473–486, Dec. 2018. https://doi.org/10.1016/j.compag.2018.10.015
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- J. Paul Cohen, “ieee8023 / covid-chestxray-dataset,” 2021.
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References
T. Gabruseva, D. Poplavskiy, and A. Kalinin, “Deep learning for automatic pneumonia detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2020-June, pp. 1436–1443, 2020. https://doi.org/10.1109/CVPRW50498.2020.00183
S. M. Ilpaj and N. Nurwati, “Analisis Pengaruh Tingkat Kematian Akibat Covid-19 Terhadap Kesehatan Mental Masyarakat Di Indonesia,” Focus : Jurnal Pekerjaan Sosial, vol. 3, no. 1, p. 16, 2020. https://doi.org/10.24198/focus.v3i1.28123
A. Sari, “Asuhan Keperawatan pada Anak: Infant (0-12 Bulan) dengan Bronkopneumonia dengan Masalah Keperawatan Ketidakefektifan Bersihan Jalan Napas di Ruang Melati RSUD Ciamis Tahun 2018,” Sekolah Tinggi Kesehatan Bhakti Kencana Bandung, 2018.
Y. Chandola, J. Virmani, H. S. Bhadauria, and P. Kumar, Deep Learning for Chest Radiographs: Computer-Aided Classification. Elsevier, 2021.
B. A. Fikri, “Analisis Faktor Risiko Pemberian Asi Dan Ventilasi Kamar Terhadap Kejadian Pneumonia Balita,” The Indonesian Journal of Public Health, vol. 11, no. 1, p. 14, 2016, doi: 10.20473/ijph.v11i1.2016.14-27.
I. Joshi, M. Grimmer, C. Rathgeb, C. Busch, F. Bremond, and A. Dantcheva, “Synthetic Data in Human Analysis: A Survey,” Aug. 2022. https://doi.org/10.48550/arXiv.2208.09191
I. M. Dendi Maysanjaya, “Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network (Classification of Pneumonia Based on Lung X-rays Images using Convolutional Neural Network),” 2020. https://doi.org/10.22146/jnteti.v9i2.66
N. Yudistira, “Peran Big Data dan Deep Learning untuk Menyelesaikan Permasalahan Secara Komprehensif,” EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi, vol. 11, no. 2, p. 78, 2021. http://dx.doi.org/10.36448/expert.v11i2.2063
F. Al-Turjman, AI-Powered IoT for COVID-19. CRC Press, 2021.
Y. F. Riti and S. S. Tandjung, “Klasifikasi Covid-19 Pada Citra CT Scans Paru-Paru Menggunakan Metode Convolution Neural Network,” Progresif: Jurnal Ilmiah Komputer, pp. 91–100, 2022. http://dx.doi.org/10.35889/progresif.v18i1.784
A. Azis, “Klasifikasi Pneumonia Menggunakan Metode Convolutional Neural Network,” Universitas Muhammadiyah Malang, 2020.
S. Motamed, P. Rogalla, and F. Khalvati, “Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images,” Inform Med Unlocked, vol. 27, no. August, p. 100779, 2021. https://doi.org/10.1016/j.imu.2021.100779
S. K. Venu and S. Ravula, “Evaluation of deep convolutional generative adversarial networks for data augmentation of chest x-ray images,” Future Internet, vol. 13, no. 1, pp. 1–13, 2021. https://doi.org/10.3390/fi13010008
N. E. M. Khalifa, M. H. N. Taha, A. E. Hassanien, and S. Elghamrawy, “Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset,” pp. 1–15, 2020. https://doi.org/10.48550/arXiv.2004.01184
S. Sundaram and N. Hulkund, GAN-based Data Augmentation for Chest X-ray Classification, vol. 1, no. 1. Association for Computing Machinery, 2021. https://doi.org/10.48550/arXiv.2107.02970
A. Solanki, A. Nayyar, and M. Naved, Generative Adversarial Networks for Image-to-Image Translation. Academic Press, 2021.
E. Ayan, B. Karabulut, and H. M. Ünver, “Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images,” Arab J Sci Eng, vol. 47, no. 2, pp. 2123–2139, Feb. 2022. https://doi.org/10.1007/s13369-021-06127-z
M. Mehra, S. Saxena, S. Sankaranarayanan, R. J. Tom, and M. Veeramanikandan, “IoT based hydroponics system using Deep Neural Networks,” Comput Electron Agric, vol. 155, pp. 473–486, Dec. 2018. https://doi.org/10.1016/j.compag.2018.10.015
L. A. Andika, H. Pratiwi, and S. S. Handajani, “Klasifikasi Penyakit Pneumonia Menggunakan Metode Convolutional Neural Network Dengan Optimasi Adaptive Momentum,” Indonesian Journal of Statistics and Its Applications, vol. 3, no. 3, pp. 331–340, 2019. https://doi.org/10.29244/ijsa.v3i3.560
J. P. Cohen et al., “TorchXRayVision: A library of chest X-ray datasets and models,” 2021. https://doi.org/10.48550/arXiv.2111.00595
P. Mooney, “Chest X-Ray Images (Pneumonia),” 2018. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed Apr. 10, 2022).
J. Paul Cohen, “ieee8023 / covid-chestxray-dataset,” 2021.
M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking,” IEEE Access, vol. 8, pp. 90847–90861, 2020. https://doi.org/10.1109/ACCESS.2020.2994222
X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf Sci (N Y), vol. 340–341, pp. 250–261, May 2016. https://doi.org/10.1016/j.ins.2016.01.033
M. Ohsaki, P. Wang, K. Matsuda, S. Katagiri, H. Watanabe, and A. Ralescu, “Confusion-matrix-based kernel logistic regression for imbalanced data classification,” IEEE Trans Knowl Data Eng, vol. 29, no. 9, pp. 1806–1819, Sep. 2017. https://doi.org/10.1109/TKDE.2017.2682249