Lung X-Ray Image Classification Using DenseNet-169 and Bayesian Optimization

Authors

  • Fayza Shahira Universitas Islam Negeri Sultan Syarif Kasim Riau , Indonesia
  • Benny Sukma Negara Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia

DOI:

https://doi.org/10.30983/knowbase.v5i1.9618

Keywords:

Deep Learning, Bayesian Optimization, Citra X-ray Dada, Optimasi Hyperparameter, Deteksi Penyakit Paru-Paru

Abstract

The increasing prevalence of lung diseases caused by infections such as Pneumonia and COVID-19 highlights the urgent need for accurate and efficient early detection methods. This study aims to improve the classification performance of chest X-ray images using the DenseNet-169 deep learning architecture, with a focus on hyperparameter optimization through Bayesian Optimization. The dataset used consists of 3,000 chest X-ray images—1,000 each for Normal, Pneumonia, and COVID-19 classes—sourced from Mendeley Data and split with an 80:20 ratio for training and testing. The baseline DenseNet-169 model initially achieved an accuracy of 96.837%, although slight overfitting was observed. By applying Bayesian Optimization, several key hyperparameters—such as learning rate, number of epochs, batch size, and kernel size—were systematically optimized. The optimized model demonstrated an improved accuracy of 97.33%, with the most notable increase in the recall score of the Normal class, which rose by 3.19% to 97%, effectively reducing the false negative rate for healthy cases. In addition, the final model recorded a precision of 99% and a specificity of 99.50% for the COVID-19 class, indicating a strong discriminative capability in identifying critical conditions. Analysis of the training and validation curves showed good convergence, confirming the effectiveness of the optimization in reducing overfitting and enhancing the model's generalization ability. Overall, the results of this study demonstrate that the application of Bayesian Optimization significantly enhances the performance of DenseNet-169 in chest X-ray image classification. The resulting model is more balanced, robust, and reliable, showing great potential for integration into AI-based automated diagnostic systems in the field of respiratory healthcare.

References

A. Dian Deva, F. Firdaus, S. Hasyim, B. Yanto, and R. Mai Candra, “Klasifikasi Prediksi Penyakit Paru-Paru Normal dengan Pneumonia berdasarkan Citra Image X-ray dengan Optimasi Adam Convolutional Neural Network (CNN).”

K. Nair, A. Deshpande, R. Guntuka, and A. Patil, “Analysing X-Ray Images to Detect Lung Diseases Using DenseNet-169 technique.” [Online]. Available: https://ssrn.com/abstract=4111864

A. D. Azzumzumi, M. Hanafi, and W. M. P. Dhuhita, “Klasifikasi Penyakit Paru-Paru Berdasarkan Peningkatan Kualitas Kontras dan EfficientNet Menggunakan Gambar X-Ray,” Teknika, vol. 13, no. 2, pp. 293–300, Jul. 2024, doi: 10.34148/teknika.v13i2.881.

L. A. Andika, H. Pratiwi, and S. S. Handajani, “KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM *,” 2019.

M. Fitriyasari, “DETEKSI COVID-19 PADA CITRA X-RAY DADA MENGGUNAKAN MACHINE LEARNING,” vol. 7, no. 1, 2022.

P. P. Dalvi, D. R. Edla, and B. R. Purushothama, “Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture,” May 01, 2023, Springer. doi: 10.1007/s42979-022-01627-7.

A. Vulli, P. N. Srinivasu, M. S. K. Sashank, J. Shafi, J. Choi, and M. F. Ijaz, “Fine‐Tuned DenseNet‐169 for Breast Cancer Metastasis Prediction Using FastAI and 1‐Cycle Policy,” Sensors, vol. 22, no. 8, Apr. 2022, doi: 10.3390/s22082988.

S. Vedhanayaki and V. Indragandhi, “A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application,” IEEE Access, vol. 12, pp. 43308–43327, 2024, doi: 10.1109/ACCESS.2024.3380188.

M. Loey, S. El-Sappagh, and S. Mirjalili, “Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data,” Comput Biol Med, vol. 142, Mar. 2022, doi: 10.1016/j.compbiomed.2022.105213.

M. Miranda, K. Valeriano, and J. Sulla-Torres, “A Detailed Study on the Choice of Hyperparameters for Transfer Learning in Covid-19 Image Datasets using Bayesian Optimization.” [Online]. Available: https://www.statista.com/statistics/1087466/

S. Kumar, “Covid19-Pneumonia-Normal Chest X-Ray Images”.

A. Vierisyah and R. Maulana Fajri, “KLASIFIKASI KANKER PARU PARU MENGGUNAKAN CNN DENGAN 5 ARSITEKTUR.”

J. Khatib Sulaiman, J. Agung Nurcahyo, T. Bayu Sasongko, U. Amikom Yogyakarta, and K. Kunci, “Hyperparameter Tuning Algoritma Supervised Learning untuk Klasifikasi Keluarga Penerima Bantuan Pangan Beras,” Indonesian Journal of Computer Science.

K. Eka Sapta Wijaya, G. Angga Pradipta, and D. Hermawan, “Optimisasi Parameter VGGNet melalui Bayesian Optimization untuk Klasifikasi Nodul Paru”.

L. Yang and A. Shami, “On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice,” Jul. 2020, doi: 10.1016/j.neucom.2020.07.061.

Y.-D. Zhang et al., “COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.”

M. A. Amou, K. Xia, S. Kamhi, and M. Mouhafid, “A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization,” Healthcare (Switzerland), vol. 10, no. 3, Mar. 2022, doi: 10.3390/healthcare10030494.

M. Baldeon calisto, J. S. Balseca Zurita, and M. A. Cruz Patiño, “COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation,” ACI Avances en Ciencias e Ingenierías, vol. 13, no. 2, p. 19, Nov. 2021, doi: 10.18272/aci.v13i2.2288.

Downloads

Published

2025-06-30

How to Cite

Shahira, F., & Negara, B. S. (2025). Lung X-Ray Image Classification Using DenseNet-169 and Bayesian Optimization. Knowbase : International Journal of Knowledge in Database, 5(1), 18–27. https://doi.org/10.30983/knowbase.v5i1.9618

Issue

Vol. 5 No. 1 (2025): June 2025

Section

Articles

Citation Check

License

Copyright (c) 2025 Fayza Shahira, Benny Sukma Negara, Febi Yanto, Suwanto Sanjaya

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).