Journal Home
Login
Register
Open Access Journal
Submit a Paper
Propose a Special lssue
Open Access
ARTICLE
Enhanced EfficientNet for Imbalanced Medical Image Classification through Grey Wolf Optimization
Issue Vol. 2 No. 02 (2025): Volume 02 Issue 02 --- Section Articles
pdf
Abstract
Medical image classification plays a pivotal role in modern diagnostics and disease management by aiding in the early detection and precise identification of various pathologies. However, a significant challenge in this domain arises from the inherent class imbalance commonly observed in medical datasets, where the number of samples for healthy cases or common conditions vastly outnumbers those for rare diseases. This imbalance often leads to deep learning models that are biased towards the majority class, resulting in suboptimal performance, particularly poor sensitivity and specificity for the critical minority classes. This article proposes a novel and robust approach to mitigate this pervasive issue by enhancing the state-of-the-art EfficientNet convolutional neural network (CNN) architecture through the application of Grey Wolf Optimization (GWO). GWO, a metaheuristic algorithm inspired by the sophisticated hunting strategies and social hierarchy of grey wolves in nature, is systematically employed to optimally tune the critical hyperparameters of the EfficientNet model. The primary aim of this optimization is to achieve superior and more balanced classification performance across all classes, especially for the underrepresented classes within imbalanced medical image data. We comprehensively detail the methodology, encompassing the meticulous handling and preprocessing of imbalanced medical image datasets, the strategic integration of the EfficientNet architecture, and the sophisticated GWO-based hyperparameter search strategy. Our experimental results, derived from rigorous evaluation, robustly demonstrate that the GWO-optimized EfficientNet significantly improves key performance metrics such as macro F1-score, balanced accuracy, and recall for minority classes. This optimized approach consistently outperforms traditional deep learning approaches that rely on manual hyperparameter tuning or fixed parameter sets. This research offers a robust, automated, and highly effective framework for developing more accurate, reliable, and clinically relevant deep learning models, thereby contributing significantly to the advancement of artificial intelligence in critical medical applications and enhancing diagnostic precision.
Keywords
References
[1] S. Liu, W. Wang, L. Deng, and H. Xu, “Cnn-trans model: a parallel dual-branch network for fundus image classification,” Biomedical Signal Processing and Control, vol. 96, Oct. 2024, doi: 10.1016/j.bspc.2024.106621.
[2] K. W. Goh et al., “Comparison of activation functions in convolutional neural network for poisson noisy image classification,” Emerging Science Journal, vol. 8, no. 2, pp. 592–602, Apr. 2024, doi: 10.28991/ESJ-2024-08-02-014.
[3] K. Man and J. Chahl, “A review of synthetic image data and its use in computer vision,” Journal of Imaging, vol. 8, no. 11, Nov. 2022, doi: 10.3390/jimaging8110310.
[4] E. T. A. Albert, N. H. Bille, and N. M. E. Leonard, “A mathematical primer to classical deep learning,” Journal of Applied and Advanced Research, vol. 9, pp. 15–25, Sep. 2024, doi: 10.21839/jaar.2024.v9.9169.
[5] A. Kaur and M. Kapoor, “An approach to recognize efficient deep learning model for pattern recognition,” in 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Mar. 2024, pp. 1–6, doi: 10.1109/ICRITO61523.2024.10522108.
[6] A. Lopes, F. P. dos Santos, D. de Oliveira, M. Schiezaro, and H. Pedrini, “Computer vision model compression techniques for embedded systems: a survey,” Computers & Graphics, vol. 123, Oct. 2024, doi: 10.1016/j.cag.2024.104015.
[7] U. Samariya and R. K. Sonker, “Comparisons of image classification using LBP with CNN and ANN,” Journal of Applied Mathematics and Computation, vol. 6, no. 3, pp. 343–346, Sep. 2022, doi: 10.26855/jamc.2022.09.006.
[8] S. Surono, M. Rivaldi, and N. Irsalinda, “Classification using u-net CN on multi-resolution CT scan image,” Fuzzy Systems and Data Mining X, A.J. Tallón-Ballesteros (Ed.), 2024, doi: 10.3233/FAIA241412.
[9] A. Meliboev, J. Alikhanov, and W. Kim, “Performance evaluation of deep learning based network intrusion detection system across multiple balanced and imbalanced datasets,” Electronics, vol. 11, no. 4, Feb. 2022, doi: 10.3390/electronics11040515.
[10] F. A. Breve, “COVID-19 detection on chest X-ray images: a comparison of CNN architectures and ensembles,” Expert Systems with Applications, vol. 204, Oct. 2022, doi: 10.1016/j.eswa.2022.117549.
[11] A. Sharma and D. Kumar, “Hyperparameter optimization in CNN: a review,” in 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Nov. 2023, pp. 237–242, doi: 10.1109/ICCCIS60361.2023.10425571.
[12] S. Surono, M. Y. F. Afitian, A. Setyawan, D. K. Eni Arofah, and A. Thobirin, “Comparison of CNN classification model using machine learning with bayesian optimizer,” HighTech and Innovation Journal, vol. 4, no. 3, pp. 531–542, Sep. 2023, doi: 10.28991/HIJ-2023-04-03-05.
[13] M. Wojciuk, Z. Swiderska-Chadaj, K. Siwek, and A. Gertych, “Improving classification accuracy of fine-tuned CNN models: impact of hyperparameter optimization,” Heliyon, vol. 10, no. 5, Mar. 2024, doi: 10.1016/j.heliyon.2024.e26586.
[14] C. J. Hellín, A. A. Olmedo, A. Valledor, J. Gómez, M. López-Benítez, and A. Tayebi, “Unraveling the impact of class imbalance on deep-learning models for medical image classification,” Applied Sciences, vol. 14, no. 8, Apr. 2024, doi: 10.3390/app14083419.
[15] P. Jeevan and A. Sethi, “Which backbone to use: a resource-efficient domain specific comparison for computer vision,” arXiv Computer Science, pp. 1–14, Jun. 2024, doi: 10.48550/arXiv.2406.05612.
[16] Y. C. Kuyu and N. Ozekmekci, “Grey wolf optimizer to the hyperparameters optimization of convolutional neural network with several activation functions,” in 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Oct. 2022, pp. 13–17, doi: 10.1109/ISMSIT56059.2022.9932838.
[17] L. V. Sari, R. P. Rosalin, and S. Uyun, “Classification fracture in X-ray images using VGG16 feature extraction and principal component analysis,” 2024 12th International Conference on Cyber and IT Service Management, CITSM 2024, pp. 1–6, 2024, doi: 10.1109/CITSM64103.2024.10775981.
[18] H. Min et al., “Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework,” Physical and Engineering Sciences in Medicine, vol. 46, no. 2, pp. 877–886, 2023, doi: 10.1007/s13246-023-01261-4.
[19] L. Zou, H. F. Lam, and J. Hu, “Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery,” Structural Health Monitoring, vol. 22, no. 4, pp. 2193–2193, Jul. 2023, doi: 10.1177/14759217221122266.
[20] M. Tan and Q. V. Le, “EfficientNetV2: smaller models and faster training,” Proceedings of Machine Learning Research, vol. 139, pp. 10096–10106, Apr. 2021, doi: 10.48550/arXiv.2104.00298.
[21] G. Zhang and W. Abdulla, “Optimizing hyperspectral imaging classification performance with CNN and batch normalization,” Applied Spectroscopy Practica, vol. 1, no. 2, Sep. 2023, doi: 10.1177/27551857231204622.
[22] L. T. Duong, P. T. Nguyen, C. Di Sipio, and D. Di Ruscio, “Automated fruit recognition using EfficientNet and MixNet,” Computers and Electronics in Agriculture, vol. 171, Apr. 2020, doi: 10.1016/j.compag.2020.105326.
[23] A. Aljohani, N. Alharbe, R. E. Al Mamlook, and M. M. Khayyat, “A hybrid combination of CNN attention with optimized random forest with grey wolf optimizer to discriminate between Arabic hateful, abusive tweets,” Journal of King Saud University - Computer and Information Sciences, vol. 36, Feb. 2024, doi: 10.1016/j.jksuci.2024.101961.
[24] Q. Xie, Z. Guo, D. Liu, Z. Chen, Z. Shen, and X. Wang, “Optimization of heliostat field distribution based on improved gray wolf optimization algorithm,” Renewable Energy, vol. 176, pp. 447–458, Oct. 2021, doi: 10.1016/j.renene.2021.05.058.
[25] R. Mohakud and R. Dash, “Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 8, pp. 6280–6291, Sep. 2022, doi: 10.1016/j.jksuci.2021.05.012.
[26] P. M. Kitonyi and D. R. Segera, “Hybrid gradient descent grey wolf optimizer for optimal feature selection,” BioMed Research International, vol. 2021, no. 1, Jan. 2021, doi: 10.1155/2021/2555622.
[27] G. Wolf and O. Gwo, Advanced optimization by nature-inspired algorithms, vol. 720. Singapore: Springer Singapore, 2018, doi: 10.1007/978-981-10-5221-7.
[28] M. C. Neves, J. Filgueiras, Z. Kokkinogenis, M. C. F. Silva, J. B. L. M. Campos, and L. P. Reis, “Enhancing experimental image quality in two-phase bubbly systems with super-resolution using generative adversarial networks,” International Journal of Multiphase Flow, vol. 180, Nov. 2024, doi: 10.1016/j.ijmultiphaseflow.2024.104952.
[29] P. I. Ritharson, K. Raimond, X. A. Mary, J. E. Robert, and A. J, “DeepRice: a deep learning and deep feature based classification of rice leaf disease subtypes,” Artificial Intelligence in Agriculture, vol. 11, pp. 34–49, Mar. 2024, doi: 10.1016/j.aiia.2023.11.001.
[30] Y. Wang et al., “PGKD-Net: prior-guided and knowledge diffusive network for choroid segmentation,” Artificial Intelligence in Medicine, vol. 150, 2024, doi: 10.1016/j.artmed.2024.102837.
[31] D. K. Saha, A. M. Joy, and A. Majumder, “YoTransViT: a transformer and CNN method for predicting and classifying skin diseases using segmentation techniques,” Informatics in Medicine Unlocked, vol. 47, 2024, doi: 10.1016/j.imu.2024.101492.
