Analisis Performa Model ResNet-50 Pada Diagnosis Pneumonia Balita Berdasarkan Citra Radiografi Thorax

Ami Rahmawati; Ita Yulianti; Siti Nurajizah; Taufik Hidayatulloh; Ani Oktarini Sari

doi:10.31294/coscience.v5i1.7618

Authors

Ami Rahmawati Universitas Nusa Mandiri
Ita Yulianti Universitas Bina Sarana Informatika
Siti Nurajizah Universitas Bina Sarana Informatika
Taufik Hidayatulloh Universitas Bina Sarana Informatika
Ani Oktarini Sari Universitas Nusa Mandiri

DOI:

https://doi.org/10.31294/coscience.v5i1.7618

Keywords:

Thorax Radiography, Pneumonia, ResNet-50

Abstract

One of the most serious complications of ARI is pneumonia, where this disease causes sufferers to experience pain when breathing and limited oxygen intake. According to the World Health Organization (WHO), pneumonia is classified as a life-threatening disease due to the high mortality rate caused. To be able to diagnose this disease, patients usually undergo various medical examination methods, one of which is through chest radiography. However, the challenge in diagnosing pneumonia generally lies in the complexity and uncertainty in interpreting the results of these methods. Therefore, this study was conducted with the aim of building an image classification model based on the Chest radiography dataset from toddler patients using the ResNet-50 architecture, which is a variant of the Convolutional Neural Networks (CNN) algorithm. The combination of the two methods is applied to analyze and process images and obtain pattern recognition with high accuracy. The research methods used include the application of data augmentation, CNN architecture design, model training, and performance evaluation. The evaluation results show that the model has quite good performance with an accuracy of 85%, which indicates the model's ability to classify images with a fairly high level of accuracy, and has the potential to help the pneumonia diagnosis process more efficiently and accurately.

References

Adzkia, M., Arland, F., Setiawan, A. W., & Korespondensi, P. (2022). Deteksi Pneumonia Menggunakan Citra Sinar-X Paru Berbasis Residual Network Detection of Pneumonia Using Residual Network Bases Lung X-Ray Image. 9(2), 373–380. https://doi.org/10.25126/jtiik.202295626

Berliani, T., Rahardja, E., & Septiana, L. (2023). Perbandingan Kemampuan Klasifikasi Citra X-ray Paru-paru menggunakan Transfer Learning ResNet-50 dan VGG-16. Journal of Medicine and Health, 5(2), 123–135. https://doi.org/10.28932/jmh.v5i2.6116

Borawar, L., & Kaur, R. (2023). ResNet: Solving Vanishing Gradient in Deep Networks. In R. P. Mahapatra, S. K. Peddoju, S. Roy, & P. Parwekar (Eds.), Proceedings of International Conference on Recent Trends in Computing (pp. 235–247). Springer Nature Singapore.

Fadhilah, M. R., & Triayudi, A. (2024). Penerapan Metode Dempster Shafer dalam Mendiagnosa Penyakit Pneumonia. KLIK: Kajian Ilmiah Informatika Dan Komputer2, 4(2).

Goyal, S., & Singh, R. (2023). Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3239–3259. https://doi.org/10.1007/s12652-021-03464-7

Halim, A. A. D., & Anraeni, S. (2021). Analisis Klasifikasi Dataset Citra Penyakit Pneumonia menggunakan Metode K-Nearest Neighbor (KNN). Indonesian Journal of Data and Science, 2(1), 01–12. https://doi.org/10.33096/ijodas.v2i1.23

Hidayatuloh, M. T., & Suharsono, T. N. (2023). Sistem Pakar Diagnosis Penyakit Infeksi Saluran Pernapasan Akut (ISPA) Menggunakan Metode Dempster Shafer. Digital Transformation Technology, 3(2), 489–498. https://doi.org/10.47709/digitech.v3i2.2894

Mooney, P. (2017). Chest X-Ray Images (Pneumonia). Www.Kaggle.Com. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia

Nurjannah, A. F., Kurniasari, A. S. D., Sari, Z., & Azhar, Y. (2022). Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(2), 330–338. https://doi.org/10.29207/resti.v6i2.4001

Onibala, I. V., & Purnomo, A. S. (2024). Sistem Pakar untuk Diagnosa Penyakit Infeksi Saluran Pernapasan Akut (ISPA) Menggunakan Metode Naive Bayes. INNOVATIVE: Journal Of Social Science Research, 4(3), 14666–14685. https://j-innovative.org/index.php/Innovative/article/view/11755/8309

Pranatha, M. D. A., Setiawan, G. H., & Maricar, M. A. (2024). Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome. Journal of Applied Informatics and Computing (JAIC), 8(2), 434–442.

Prasetyo, A. R., Sussi, & Aditya, B. (2023). Analisis Perbandingan Algoritma Support Vector Machine (Svm) Dan Convolutional Neural Network (Cnn) Untuk Sistem Deteksi Katarak. Jurnal Ilmiah Teknik Mesin, Elektro Dan Komputer, 3(1), 1–10. https://doi.org/10.51903/juritek.v3i1.604

Praskatama, V., Sari, C. A., Rachmawanto, E. H., & Mohd Yaacob, N. (2023). Pneumonia Prediction Using Convolutional Neural Network. Jurnal Teknik Informatika (Jutif), 4(5), 1217–1226. https://doi.org/10.52436/1.jutif.2023.4.5.1353

Reshan, M. S. Al, Gill, K. S., Anand, V., Gupta, S., Alshahrani, H., Sulaiman, A., & Shaikh, A. (2023). Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model. Healthcare (Switzerland), 11(11). https://doi.org/10.3390/healthcare11111561

Sholihah, L. (2021). Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network. Seminar Nasional Fortei7-4, 4(1), 670.

Sulistio, M. J., & Lubis, C. (2023). Implementasi CNN dan MobileNet untuk Mendeteksi Penyakit Pneumonia dan COVID-19 dengan Menggunakan Aplikasi Smartphone. Nusantara Journal of Multidisciplinary Science, 1(4), 736–745.

Truong, T. X., Nhu, V., Phuong, D. T. N., Nghi, L. T., Hung, N. N., Hoa, P. V., & Bui, D. T. (2023). A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas. Remote Sensing, 15(14), 1–21.

Widiarto, S. A., Saputra, W. A., & Dewi, A. R. (2021). Klasifikasi Citra X-Ray Toraks Dengan Menggunakan Contrast Limited Adaptive Histogram Equalization Dan Convolutional Neural Network (Studi Kasus: Pneumonia). JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 6(2), 348–359. https://doi.org/10.29100/jipi.v6i2.2102

Yadav, K. K., & Awasthi, S. (2023). Childhood Pneumonia: What’s Unchanged, and What’s New? In Indian Journal of Pediatrics (Vol. 90, Issue 7, pp. 693–699). Springer. https://doi.org/10.1007/s12098-023-04628-3

Zahir, M., & Adi Saputra, R. (2024). Deteksi Penyakit Retinopati Diabetes Menggunakan Citra Mata Dengan Implementasi Deep Learning Cnn. Jurnal Teknoinfo, 18(1), 121–132.

Zalukhu, E. (2024). Analisa Perbandingan Metode Certainly Faktor-Naive Bayes Terhadap Diagnose Penyakit Pneumonia. ADA Journal of Information System Research, 1(3).