Lightweight Deep Learning Models for Lung Disease Classification Using Chest X-ray Images


Abdullah Sholum(1*); Aji Prasetya Wibawa(2); Ardhana Putra Agustavada(3); Dafa Fadhilah Hilmi(4); Felix Andika Dwiyanto(5);

(1) Universitas Negeri Malang
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(4) Universitas Negeri Malang
(5) AGH University of Krak´ow
(*) Corresponding Author

  

Abstract


Lung disease classification from chest X-ray images using deep learning has attracted significant attention due to its potential to support rapid and automated medical diagnosis. However, many deep learning models require high computational resources, limiting their applicability in resource-constrained environments. This study evaluates the effectiveness of lightweight deep learning architectures for lung disease classification using chest X-ray images consisting of COVID-19, Normal, and Pneumonia classes. Three architectures were comparatively analyzed, including a baseline Convolutional Neural Network (CNN), EfficientNetV2, and MobileNetV2. All models were trained and evaluated under identical preprocessing and experimental conditions using a dataset of 697 chest X-ray images with a 60:20:20 split ratio for training, validation, and testing. Experimental results show that EfficientNetV2 and MobileNetV2 achieved classification accuracy up to 0.99 with AUC-ROC values approaching 1.0. In terms of computational efficiency, both lightweight architectures reduced trainable parameters by approximately 99.3% compared to the baseline CNN, decreasing from 576,851 to 3,843 trainable parameters through transfer learning with frozen pretrained layers. MobileNetV2 achieved this performance with 0.613 GFLOPs and a model size of 9,435 KB, while EfficientNetV2 required 0.781 GFLOPs and 15,993 KB. Although EfficientNetV2 demonstrated slightly more stable performance, MobileNetV2 provided the most effective trade-off between classification accuracy and computational efficiency, making it more suitable for deployment in resource-constrained healthcare environments. These findings demonstrate the feasibility of lightweight deep learning architectures for efficient and reliable lung disease classification from chest X-ray images

Keywords


Chest X-ray; CNN; Deep learning; Lung Disease; Lightweight Architectures; Medical Image Analysis

  
  

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doi  https://doi.org/10.33096/ilkom.v18i2.3351.302-319 		  

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J. Oh et al., “Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study,” Nat. Med., vol. 32, no. 1, pp. 197–223, Jan. 2026, doi: 10.1038/s41591-025-04077-9.

H. Gan et al., “Smoking: a leading factor for the death of chronic respiratory diseases derived from Global Burden of Disease Study 2019,” BMC Pulm. Med., vol. 22, no. 1, p. 149, Dec. 2022, doi: 10.1186/s12890-022-01944-w.

I. Ahmad et al., “New paradigms to break barriers in early cancer detection for improved prognosis and treatment outcomes,” J. Gene Med., vol. 26, no. 8, Aug. 2024, doi: 10.1002/jgm.3730.

D. Raval et al., “Cost-effectiveness analysis of AI-assisted chest X-ray interpretation tools for TB screening: a rapid HTA,” Front. Digit. Health, vol. 7, Dec. 2025, doi: 10.3389/fdgth.2025.1629127.

H. H. Pham, H. Q. Nguyen, H. T. Nguyen, L. T. Le, and L. Khanh, “An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph,” IEEE Access, vol. 10, pp. 104512–104531, 2022, doi: 10.1109/ACCESS.2022.3210468.

T. Mahmood, A. Rehman, T. Saba, L. Nadeem, and S. A. O. Bahaj, “Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification,” IEEE Access, vol. 11, pp. 113623–113652, 2023, doi: 10.1109/ACCESS.2023.3313977.

R. Sarki, K. Ahmed, H. Wang, Y. Zhang, and K. Wang, “Automated detection of COVID-19 through convolutional neural network using chest x-ray images,” PLoS One, vol. 17, no. 1, p. e0262052, Jan. 2022, doi: 10.1371/journal.pone.0262052.

V. Ravi, V. Acharya, and M. Alazab, “A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images,” Cluster Comput., vol. 26, no. 2, pp. 1181–1203, Apr. 2023, doi: 10.1007/s10586-022-03664-6.

G. A. Sandag and D. T. Kabo, “Comparative Analysis of Lung Cancer Classification Models Using EfficientNet and ResNet on CT-Scan Lung Images,” CogITo Smart Journal, vol. 10, no. 1, pp. 259–270, Jun. 2024, doi: 10.31154/cogito.v10i1.706.680-691.

M. N. Akhtar, A. Haleem, and M. Javaid, “Scope of health care system in rural areas under Medical 4.0 environment,” Intelligent Pharmacy, vol. 1, no. 4, pp. 217–223, Dec. 2023, doi: 10.1016/j.ipha.2023.07.003.

V.-T. Hoang and K.-H. Jo, “Practical Analysis on Architecture of EfficientNet,” in 2021 14th International Conference on Human System Interaction (HSI), IEEE, Jul. 2021, pp. 1–4. doi: 10.1109/HSI52170.2021.9538782.

S. Muhammad Raza, S. Murtaza Hussain Abidi, M. Masuduzzaman, and S. Y. Shin, “DSConvNet: A Lightweight Architecture for Extracting Image Features From Depthwise Separable Convolution Network for Edge Devices,” IEEE Access, vol. 13, pp. 210102–210116, 2025, doi: 10.1109/ACCESS.2025.3639410.

M. Chowdhury, S. R. Shrima, and M. S. Islam, “Comparative analysis of deep learning architectures for multi-class mineral classification: a study using EfficientNet and ResNet models,” Earth Sci. Inform., vol. 18, no. 3, p. 485, Sep. 2025, doi: 10.1007/s12145-025-01983-x.

K. Kansal, T. B. Chandra, and A. Singh, “ResNet-50 vs. EfficientNet-B0: Multi-Centric Classification of Various Lung Abnormalities Using Deep Learning,” Procedia Comput. Sci., vol. 235, pp. 70–80, 2024, doi: 10.1016/j.procs.2024.04.007.

H. Louati, A. Louati, K. Mansour, and E. Kariri, “Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs,” IEEE Access, vol. 13, pp. 10070–10082, 2025, doi: 10.1109/ACCESS.2025.3529206.

A. S. Nour, C. Raymond, D. Zewdneh, and U. Anazodo, “Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the African healthcare system,” Pediatr. Radiol., Jan. 2026, doi: 10.1007/s00247-025-06504-y.

F. Alshanketi et al., “Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets,” Journal of Imaging Informatics in Medicine, vol. 38, no. 4, pp. 2021–2040, Nov. 2024, doi: 10.1007/s10278-024-01334-0.

“Deployable and interpretable deep learning architectures: navigating clinical challenges in medical image segmentation,” 2024. doi: 10.14264/98c85ec.

G. Kornaros, “Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective,” IEEE Access, vol. 10, pp. 58603–58622, 2022, doi: 10.1109/ACCESS.2022.3179047.

H. Abdel-Jaber, D. Devassy, A. Al Salam, L. Hidaytallah, and M. EL-Amir, “A Review of Deep Learning Algorithms and Their Applications in Healthcare,” Algorithms, vol. 15, no. 2, p. 71, Feb. 2022, doi: 10.3390/a15020071.

P. Arroba, R. Buyya, R. Cárdenas, J. L. Risco‐Martín, and J. M. Moya, “Sustainable edge computing: Challenges and future directions,” Softw. Pract. Exp., vol. 54, no. 11, pp. 2272–2296, Nov. 2024, doi: 10.1002/spe.3340.

A. Biglari and W. Tang, “A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme,” Sensors, vol. 23, no. 4, p. 2131, Feb. 2023, doi: 10.3390/s23042131.

A. Sivanathan et al., “Real-Time and Trustworthy Classification of IoT Traffic Using Lightweight Deep Learning,” IEEE Trans. Netw. Sci. Eng., vol. 13, pp. 3256–3273, 2026, doi: 10.1109/TNSE.2025.3628913.

C. C. Yang, “Explainable Artificial Intelligence for Predictive Modeling in Healthcare,” J. Healthc. Inform. Res., vol. 6, no. 2, pp. 228–239, Jun. 2022, doi: 10.1007/s41666-022-00114-1.

J. Kim, “Quantization Robust Pruning With Knowledge Distillation,” IEEE Access, vol. 11, pp. 26419–26426, 2023, doi: 10.1109/ACCESS.2023.3257864.

Adewale Abayomi Adeniran, Amaka Peace Onebunne, and Paul William, “Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making,” World Journal of Advanced Research and Reviews, vol. 23, no. 3, pp. 2447–2658, Sep. 2024, doi: 10.30574/wjarr.2024.23.3.2936.

E. Hassan, M. S. Hossain, A. Saber, S. Elmougy, A. Ghoneim, and G. Muhammad, “A quantum convolutional network and ResNet (50)-based classification architecture for the MNIST medical dataset,” Biomed. Signal Process. Control, vol. 87, p. 105560, Jan. 2024, doi: 10.1016/j.bspc.2023.105560.

P. Sharma and V. Sharma, “Classification of COVID-19 Utilizing CT Scan Images Employing the EfficientNet Model,” in 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), IEEE, Sep. 2024, pp. 1–7. doi: 10.1109/ACROSET62108.2024.10743990.

A. Assis, J. Dantas, and E. Andrade, “The performance-interpretability trade-off: a comparative study of machine learning models,” J. Reliab. Intell. Environ., vol. 11, no. 1, p. 1, Mar. 2025, doi: 10.1007/s40860-024-00240-0.

M. A. Abdou, “Literature review: efficient deep neural networks techniques for medical image analysis,” Neural Comput. Appl., vol. 34, no. 8, pp. 5791–5812, Apr. 2022, doi: 10.1007/s00521-022-06960-9.

E. Ostrowski and M. Shafique, “J-Net: A Low-Resolution Lightweight Neural Network for Semantic Segmentation in the Medical Field for Embedded Deployment,” 2025, pp. 480–492. doi: 10.1007/978-3-031-77392-1_36.

M. Karki et al., “Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays,” Diagnostics, vol. 12, no. 1, p. 188, Jan. 2022, doi: 10.3390/diagnostics12010188.

S. Somvanshi et al., “From Tiny Machine Learning to Tiny Deep Learning: A Survey,” ACM Comput. Surv., vol. 58, no. 7, pp. 1–33, May 2026, doi: 10.1145/3776588.

F. Ma, Z. Ma, B. Sun, and S. Li, “TA-CNN,” in Proceedings of the 30th ACM International Conference on Multimedia, New York, NY, USA: ACM, Oct. 2022, pp. 7099–7103. doi: 10.1145/3503161.3551587.

J. Li, H. Sun, and J. Li, “Beyond confusion matrix: learning from multiple annotators with awareness of instance features,” Mach. Learn., vol. 112, no. 3, pp. 1053–1075, Mar. 2023, doi: 10.1007/s10994-022-06211-x.

A. Assis, J. Dantas, and E. Andrade, “The performance-interpretability trade-off: a comparative study of machine learning models,” J. Reliab. Intell. Environ., vol. 11, no. 1, p. 1, Mar. 2025, doi: 10.1007/s40860-024-00240-0.

S. Riyadi, R. Mulya, and A. Nabila Realisti, “Comparison of MobilenetV2 and EfficiennetB3 Method to Classify Diseases on Corn Leaves,” E3S Web of Conferences, vol. 595, p. 02006, Nov. 2024, doi: 10.1051/e3sconf/202459502006.

M. Rajkumar, S. R. Siriyala, A. Sharma, and D. N. S. Kumar, “Comparative Analysis of ResNet, EfficientNet, and MobileNetV2 for Detecting Skin Conditions in Deep Learning Models,” in 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), IEEE, Dec. 2024, pp. 1487–1492. doi: 10.1109/ICSCNA63714.2024.10864030.

C. Sorino, D. Ceriani, and S. Agati, “Acute respiratory failure in a patient with diffuse ground-glass opacities during the COVID-19 pandemic,” in Rare and Interstitial Lung Diseases, Elsevier, 2025, pp. 245–256. doi: 10.1016/B978-0-323-93522-7.00030-6.

W. Cardoso, T. Barros, G. Gonçalves, C. Premebida, and U. J. Nunes, “Multispectral Image Segmentation in Agriculture: Evaluating Deep Learning Models with Train-Test Split and Cross-Validation Strategies,” in 2024 7th Iberian Robotics Conference (ROBOT), IEEE, Nov. 2024, pp. 1–6. doi: 10.1109/ROBOT61475.2024.10797395.

B. Billot, C. Magdamo, Y. Cheng, S. E. Arnold, S. Das, and J. E. Iglesias, “Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets,” Proceedings of the National Academy of Sciences, vol. 120, no. 9, Feb. 2023, doi: 10.1073/pnas.2216399120.

A. J. Abdi et al., “Standardisation and Optimisation of Chest and Pelvis X-Ray Imaging Protocols Across Multiple Radiography Systems in a Radiology Department,” Diagnostics, vol. 15, no. 12, p. 1450, Jun. 2025, doi: 10.3390/diagnostics15121450.

M. Sahlabadi, R. C. Muniyandi, Z. Shukur, and F. Qamar, “Lightweight Software Architecture Evaluation for Industry: A Comprehensive Review,” Sensors, vol. 22, no. 3, p. 1252, Feb. 2022, doi: 10.3390/s22031252.

J. Padhi, L. Korada, A. Dash, P. K. Sethy, S. K. Behera, and A. Nanthaamornphong, “Paddy Leaf Disease Classification Using EfficientNet B4 With Compound Scaling and Swish Activation: A Deep Learning Approach,” IEEE Access, vol. 12, pp. 126426–126437, 2024, doi: 10.1109/ACCESS.2024.3451557.

S. Mehta and A. Singh, “Harnessing MobileNetV2 for Real-Time Detection of Citrus Diseases in Resource-Constrained Environments,” in 2025 International Conference on Computing Technologies (ICOCT), IEEE, Jun. 2025, pp. 1–5. doi: 10.1109/ICOCT64433.2025.11118771.

P. Del Moral, S. Nowaczyk, and S. Pashami, “Why Is Multiclass Classification Hard?,” IEEE Access, vol. 10, pp. 80448–80462, 2022, doi: 10.1109/ACCESS.2022.3192514.

K. Kansal, T. B. Chandra, and A. Singh, “Deep Learning Approaches for Multi-Class Lung Disease Classification Utilizing EfficientNet Architectures and CXR Images,” Procedia Comput. Sci., vol. 260, pp. 175–181, 2025, doi: 10.1016/j.procs.2025.03.191.

T. Sanida and M. Dasygenis, “A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system,” Applied Intelligence, vol. 54, no. 6, pp. 4756–4780, Mar. 2024, doi: 10.1007/s10489-024-05420-2.

C.-T. Yen and C.-Y. Tsao, “Lightweight convolutional neural network for chest X-ray images classification,” Sci. Rep., vol. 14, no. 1, p. 29759, Nov. 2024, doi: 10.1038/s41598-024-80826-z.


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