Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models

(1) * Simeon Yuda Prasetyo Mail (Bina Nusantara University, Indonesia)
(2) Ghinaa Zain Nabiilah Mail (Bina Nusantara University, Indonesia)
(3) Zahra Nabila Izdihar Mail (Bina Nusantara University, Indonesia)
(4) Sani Muhamad Isa Mail (Bina Nusantara University, Indonesia)
*corresponding author


Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgments. In addition, the process of diagnosis requires a longer time. One technique can be applied by applying advanced deep learning, namely, Transfer Learning with Deep Convolutional Neural Network (Deep CNN) and modified Multilevel Meta Ensemble Learning using Softmax. The purpose of this research was to improve the accuracy of the pneumonia classification model. This study proposes a classification model with a meta-ensemble approach using five classification algorithms: Xception, Resnet 15V2, InceptionV3, VGG16, and VGG19. The ensemble stage used two different concepts, where the first level ensemble combined the output of the Xception, ResNet15V2, and InceptionV3 algorithms. Then the output from the first ensemble level is reused for the following learning process, combined with the output from other algorithms, namely VGG16 and VGG19. This process is called ensemble level two. The classification algorithm used at this stage is the same as the previous stage, using KNN as a classification model. Based on experiments, the model proposed in this study has better accuracy than the others, with a test accuracy value of 98.272%. The benefit of this research could help doctors as a recommendation tool to make more accurate and timely diagnoses, thus speeding up the treatment process and reducing the risk of complications.


Pneumonia classification; Transfer learning; Deep convolutional neural network; Softmax; Multilevel meta ensemble



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[1] “Pneumonia in children,” World Health Organization, 2022. Accessed Dec. 01, 2022. [Online]. Available at:

[2] Kementerian Kesehatan Republik Indonesia,“ Pneumonia Pada Anak bisa Dicegah dan Diobati", 2020. Accessed May 01, 2022. [Online]. Available at:

[3] W. Khan, N. Zaki, and L. Ali, “Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 51747–51771, Jul. 2021, doi: 10.1109/ACCESS.2021.3069937.

[4] O. Stephen, M. Sain, U. J. Maduh, and D.-U. Jeong, “An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare,” J. Healthc. Eng., vol. 2019, pp. 1–7, Mar. 2019, doi: 10.1155/2019/4180949.

[5] T. B. Chandra and K. Verma, “Pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm,” in Advances in Intelligent Systems and Computing, vol. 1022 AISC, Springer Science and Business Media Deutschland GmbH, 2020, pp. 21–33, doi: 10.1007/978-981-32-9088-4_3.

[6] N. Sharma, V. Jain, and A. Mishra, “An Analysis Of Convolutional Neural Networks For Image Classification,” Procedia Comput. Sci., vol. 132, pp. 377–384, Jan. 2018, doi: 10.1016/j.procs.2018.05.198.

[7] P. Chagas et al., “Evaluation of Convolutional Neural Network Architectures for Chart Image Classification,” in 2018 International Joint Conference on Neural Networks (IJCNN), Jul. 2018, vol. 2018-July, pp. 1–8, doi: 10.1109/IJCNN.2018.8489315.

[8] Z. Wang et al., “Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features,” IEEE Access, vol. 7, pp. 105146–105158, 2019, doi: 10.1109/ACCESS.2019.2892795.

[9] W. Alakwaa, M. Nassef, and A. Badr, “Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN),” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 8, pp. 66–73, 2017, doi: 10.14569/IJACSA.2017.080853.

[10] J. C. Hung, K.-C. Lin, and N.-X. Lai, “Recognizing learning emotion based on convolutional neural networks and transfer learning,” Appl. Soft Comput., vol. 84, p. 105724, Nov. 2019, doi: 10.1016/j.asoc.2019.105724.

[11] F. Zhuang et al., “A Comprehensive Survey on Transfer Learning,” Proc. IEEE, vol. 109, no. 1, pp. 43–76, Jan. 2021, doi: 10.1109/JPROC.2020.3004555.

[12] E. Ayan and H. M. Unver, “Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning,” in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Apr. 2019, pp. 1–5, doi: 10.1109/EBBT.2019.8741582.

[13] D. Zhang, F. Ren, Y. Li, L. Na, and Y. Ma, “Pneumonia Detection from Chest X-ray Images Based on Convolutional Neural Network,” Electronics, vol. 10, no. 13, p. 1512, Jun. 2021, doi: 10.3390/electronics10131512.

[14] P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” arXiv Comput. Vis. Pattern Recognit., pp. 1–7, Nov. 2017, doi: 10.48550/arXiv.1711.05225.

[15] T. Rahman et al., “Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray,” Appl. Sci., vol. 10, no. 9, p. 3233, May 2020, doi: 10.3390/app10093233.

[16] D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, “Pneumonia Detection Using CNN based Feature Extraction,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Feb. 2019, pp. 1–7, doi: 10.1109/ICECCT.2019.8869364.

[17] V. Chouhan et al., “A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images,” Appl. Sci., vol. 10, no. 2, p. 559, Jan. 2020, doi: 10.3390/app10020559.

[18] A. Mabrouk, R. P. Díaz Redondo, A. Dahou, M. Abd Elaziz, and M. Kayed, “Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks,” Appl. Sci., vol. 12, no. 13, p. 6448, Jun. 2022, doi: 10.3390/app12136448.

[19] J. R. Ferreira, D. Armando Cardona Cardenas, R. A. Moreno, M. de Fatima de Sa Rebelo, J. E. Krieger, and M. Antonio Gutierrez, “Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images,” in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020, vol. 2020-July, pp. 1238–1241, doi: 10.1109/EMBC44109.2020.9176517.

[20] “Chest X-Ray Images (Pneumonia),” Kaggle. Accessed May 02, 2018. [Online]. Available at:

[21] D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, Feb. 2018, doi: 10.1016/j.cell.2018.02.010.

[22] H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, and V. Singh, “Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet,” Chaos, Solitons & Fractals, vol. 138, p. 109944, Sep. 2020, doi: 10.1016/j.chaos.2020.109944.

[23] H. Polat and H. Danaei Mehr, “Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture,” Appl. Sci., vol. 9, no. 5, p. 940, Mar. 2019, doi: 10.3390/app9050940.

[24] A. Mortazi and U. Bagci, “Automatically Designing CNN Architectures for Medical Image Segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11046 LNCS, Springer Verlag, 2018, pp. 98–106, doi: 10.1007/978-3-030-00919-9_12.

[25] C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, vol. 07-12-June, pp. 1–9, doi: 10.1109/CVPR.2015.7298594.

[26] M. I. Sarker, H. Kim, D. Tarasov, and D. Akhmetzanov, “Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images,” arXiv Image Video Process., vol. 1–8, Dec. 2019, doi: 10.48550/arXiv.1912.04619.

[27] F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, vol. 2017-Janua, pp. 1800–1807, doi: 10.1109/CVPR.2017.195.

[28] S. H. Kassani, P. H. Kassani, R. Khazaeinezhad, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Diabetic Retinopathy Classification Using a Modified Xception Architecture,” in 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2019, pp. 1–6, doi: 10.1109/ISSPIT47144.2019.9001846.

[29] A. Sengupta, Y. Ye, R. Wang, C. Liu, and K. Roy, “Going Deeper in Spiking Neural Networks: VGG and Residual Architectures,” Front. Neurosci., vol. 13, p. 95, Mar. 2019, doi: 10.3389/fnins.2019.00095.

[30] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Sep. 2014, pp. 1–14, doi: 10.48550/arXiv.1409.1556.

[31] D. Sarwinda, R. H. Paradisa, A. Bustamam, and P. Anggia, “Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer,” Procedia Comput. Sci., vol. 179, pp. 423–431, Jan. 2021, doi: 10.1016/j.procs.2021.01.025.

[32] Z.-H. Zhou, “Ensemble Learning,” in Machine Learning, Singapore: Springer Singapore, 2021, pp. 181–210, doi: 10.1007/978-981-15-1967-3_8.

[33] A. Almasri, E. Celebi, and R. S. Alkhawaldeh, “EMT: Ensemble Meta-Based Tree Model for Predicting Student Performance,” Sci. Program., vol. 2019, pp. 1–13, Feb. 2019, doi: 10.1155/2019/3610248.

[34] W. Xing and Y. Bei, “Medical Health Big Data Classification Based on KNN Classification Algorithm,” IEEE Access, vol. 8, pp. 28808–28819, 2020, doi: 10.1109/ACCESS.2019.2955754.

[35] R. Yacouby and D. Axman, “Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models,” in Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, Nov. 2020, pp. 79–91, doi: 10.18653/v1/2020.eval4nlp-1.9.

[36] M. Liebenlito, Y. Irene, and A. Hamid, “Classification of Tuberculosis and Pneumonia in Human Lung Based on Chest X-Ray Image using Convolutional Neural Network,” Inpr. Indones. J. Pure Appl. Math., vol. 2, no. 1, pp. 24–32, Mar. 2020, doi: 10.15408/inprime.v2i1.14545.

[37] S. Sharma and K. Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks,” Procedia Comput. Sci., vol. 218, pp. 357–366, Jan. 2023, doi: 10.1016/j.procs.2023.01.018.

[38] 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, doi: 10.1007/s13369-021-06127-z.

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