Optimizing ResNet50 for Medical Image Classification: A Comparative Study of Ghost Modules, Pruning, and Knowledge Distillation
Received: 29 July 2025 | Revised: 11 August 2025 | Accepted: 19 August 2025 | Online: 28 November 2025
Corresponding author: Kadhim Aseel Nadhum
Abstract
Computed Tomography (CT) imaging plays a vital role in assessing lung disease severity in COVID-19 patients. However, deploying deep learning models such as ResNet50 for automated severity classification (mild vs. severe) remains challenging in resource-constrained medical environments due to their high computational demands. This study presents a comparative analysis of three optimization techniques, namely ghost modules, pruning, and knowledge distillation, to enhance the efficiency of the ResNet50 model while maintaining high diagnostic accuracy. The optimized models were trained and evaluated using a real-world dataset comprising 817 CT images collected from public hospitals in Babylon Province, Iraq. Experimental work indicates that a GhostNet-augmented ResNet50 improved to a record 98.4% accuracy, a knowledge distillation-based variant achieved 93%, and a pruned ResNet50 variant achieved 91%. These results indicate a better balance between performance and computation achieved by ghost modules, supporting their particular relevance to real-time applications within resource-constrained medical systems.
Keywords:
COVID-19, CT classification, ResNet50 optimization, ghost modulesDownloads
References
A. I. Alaiad, E. A. Mugdadi, I. I. Hmeidi, N. Obeidat, and L. Abualigah, "Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning," Journal of Medical and Biological Engineering, vol. 43, no. 2, pp. 135–146, Apr. 2023. DOI: https://doi.org/10.1007/s40846-023-00783-2
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
Y. Xu, T. M. Khan, Y. Song, and E. Meijering, "Edge deep learning in computer vision and medical diagnostics: a comprehensive survey," Artificial Intelligence Review, vol. 58, no. 3, Jan. 2025, Art. no. 93. DOI: https://doi.org/10.1007/s10462-024-11033-5
K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, "GhostNet: More Features From Cheap Operations," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, Jun. 2020, pp. 1577–1586. DOI: https://doi.org/10.1109/CVPR42600.2020.00165
C. T. Pham, M. N. Phan, and T. T. T. Tran, "Image classification based on deep learning with automatic relevance determination and structured Bayesian pruning," Computer Research and Modeling, vol. 16, no. 4, pp. 927–938, Aug. 2024. DOI: https://doi.org/10.20537/2076-7633-2024-16-4-927-938
S. Wang et al., "Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation." arXiv, Jun. 27, 2025.
K. A. Nadhum, S. M. Sam, and S. Usman, "Prediction Model Using Deep Learning for Lung Illness Severity Among Covid-19 Patients in Iraq," in 2024 5th International Conference on Smart Sensors and Application (ICSSA), Penang, Malaysia, Sep. 2024, pp. 1–6. DOI: https://doi.org/10.1109/ICSSA62312.2024.10788660
H. Alshazly, C. Linse, E. Barth, and T. Martinetz, "Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning," Sensors, vol. 21, no. 2, Jan. 2021, Art. no. 455. DOI: https://doi.org/10.3390/s21020455
A. N. Kadhum, "Performance Assessment of DL Model in Iraq for Covid-19 Patient Severity Prediction from X-Ray Scan Images," Journal of Image Processing and Intelligent Remote Sensing, no. 36, pp. 1–11, Sep. 2023. DOI: https://doi.org/10.55529/jipirs.36.1.11
N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022. DOI: https://doi.org/10.48084/etasr.4613
Z. Al-Milaji and H. Yousif, "Lightweight Deep Learning Model Optimization for Medical Image Analysis," International Journal of Imaging Systems and Technology, vol. 34, no. 5, 2024, Art. no. e23173. DOI: https://doi.org/10.1002/ima.23173
Y. He and L. Xiao, "Structured Pruning for Deep Convolutional Neural Networks: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2900–2919, Feb. 2024. DOI: https://doi.org/10.1109/TPAMI.2023.3334614
N. K. Dinsdale, M. Jenkinson, and A. I. L. Namburete, "STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation," Medical Image Analysis, vol. 81, Oct. 2022, Art. no. 102583. DOI: https://doi.org/10.1016/j.media.2022.102583
H. Meng, Z. Lin, F. Yang, Y. Xu, and L. Cui, "Knowledge Distillation In Medical Data Mining: A Survey," in 5th International Conference on Crowd Science and Engineering, Jinan, China, Nov. 2022, pp. 175–182. DOI: https://doi.org/10.1145/3503181.3503211
A. BabaAhmadi, S. Khalafi, M. ShariatPanahi, and M. Ayati, "Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach," Iran Journal of Computer Science, vol. 7, no. 2, pp. 177–187, Jun. 2024. DOI: https://doi.org/10.1007/s42044-023-00167-4
E. Cengil and A. Cinar, "Multiple Classification of Flower Images Using Transfer Learning," in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey, Sep. 2019, pp. 1–6. DOI: https://doi.org/10.1109/IDAP.2019.8875953
Y. Fu, Y. Lei, T. Wang, W. J. Curran, T. Liu, and X. Yang, "Deep learning in medical image registration: a review," Physics in Medicine & Biology, vol. 65, no. 20, Jul. 2020, Art. no. 20TR01. DOI: https://doi.org/10.1088/1361-6560/ab843e
B. Mandal, A. Okeukwu, and Y. Theis, "Masked Face Recognition using ResNet-50." arXiv, 2021.
T. N. Nguyen, T. T. Nguyen, T. H. Nguyen, and B. V. Ngo, "A Robust Approach for Breast Cancer Classification from DICOM Images," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 23499–23505, Jun. 2025. DOI: https://doi.org/10.48084/etasr.10931
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Copyright (c) 2025 Kadhim Aseel Nadhum, Suriani Binti Mohd Sam, Sahnius Bt Usman
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