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Parameter-Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images
Corresponding Author(s) : Hasanatul Iftitah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026
Abstract
Malaria diagnostic automation faces critical challenges, including severe class imbalance with ratios of up to 54:1, limited datasets containing 200 to 500 images, and computational inefficiency resulting from the need to train separate models for each detection-classification combination. This study developed a multi-model framework with a shared classification architecture that trains classification models once on ground-truth crops and reuses them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved mAP@50 scores ranging from 70.84% to 96.27%, with high recall values of 71.05% to 93.12% minimizing missed parasite detections. Classification results demonstrated the importance of dataset-dependent model selection, with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved accuracies of 91.51% on the IML Lifecycle dataset and 98.28% on the MP-IDB Species dataset, while EfficientNet-B0 achieved 86.45% on the multi-patient MD-2019 dataset. ResNet50 achieved 96.13% accuracy on severely imbalanced MP-IDB Stages dataset. Focal Loss optimization with alpha = 1.0 and gamma = 1.5 enabled robust minority-class performance, achieving F1-scores between 0.44 and 1.00 on ultra-minority classes and demonstrating effective handling of class imbalance. The compact models, with sizes ranging from 46 MB to 89 MB, enable practical deployment on resource-constrained hardware.
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- W. H. Organization, World malaria report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization, 2024.
- World Health Organization, “Global technical strategy for malaria 2016–2030, 2021 update,” 2021. Accessed: May 23, 2025.
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References
W. H. Organization, World malaria report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization, 2024.
World Health Organization, “Global technical strategy for malaria 2016–2030, 2021 update,” 2021. Accessed: May 23, 2025.
H. Sutanto, “Combating Malaria with Vaccines: Insights from the One Health Framework,” Sep. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/amh69030015
S. Rajaraman, S. Jaeger, and S. K. Antani, “Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images,” PeerJ, vol. 7, May 2019. https://doi.org/10.7717/PEERJ.6977
Q. A. Arshad et al., “A dataset and benchmark for malaria life-cycle classification in thin blood smear images,” Neural Comput. Appl., vol. 34, no. 6, pp. 4473–4485, 2022. https://doi.org/10.1007/s00521-021-06602-6
A. Loddo, C. Fadda, and C. Di Ruberto, “An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis,” J. Imaging, vol. 8, no. 3, 2022. https://doi.org/10.3390/jimaging8030066
L. Zedda, A. Loddo, and C. Di Ruberto, “A Deep Learning Based Framework for Malaria Diagnosis on High Variation Data Set,” in Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, and F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 358–370. https://doi.org/10.1007/978-3-031-06430-2_30
L. Zedda, A. Loddo, and C. Di Ruberto, “YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection,” J. Imaging, vol. 9, no. 12, 2023. https://doi.org/10.3390/jimaging9120266
D. Sukumarran et al., “An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images,” Parasit. Vectors, vol. 17, no. 1, p. 188, 2024. https://doi.org/10.1186/s13071-024-06215-7
E. Pachetti and S. Colantonio, “A systematic review of few-shot learning in medical imaging,” Artif. Intell. Med., vol. 156, p. 102949, 2024. https://doi.org/https://doi.org/10.1016/j.artmed.2024.102949
F. B. Tek, A. G. Dempster, and I. Kale, “Computer vision for microscopy diagnosis of malaria,” Malar. J., vol. 8, no. 1, 2009. https://doi.org/10.1186/1475-2875-8-153
M. Salmi, D. Atif, D. Oliva, A. Abraham, and S. Ventura, “Handling imbalanced medical datasets: review of a decade of research,” Artif. Intell. Rev., vol. 57, no. 10, p. 273, 2024. https://doi.org/10.1007/s10462-024-10884-2
F. Yang et al., “Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears,” IEEE J. Biomed. Health Inform., vol. 24, no. 5, pp. 1427–1438, 2020. https://doi.org/10.1109/JBHI.2019.2939121
K. Alkandary, A. S. Yildiz, and H. Meng, “A Comparative Study of YOLO Series (v3–v10) with DeepSORT and StrongSORT: A Real-Time Tracking Performance Study,” Electronics (Basel)., vol. 14, no. 5, 2025. https://doi.org/10.3390/electronics14050876
X. Li et al., “Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, in NIPS ’20. Red Hook, NY, USA: Curran Associates Inc., 2020.
R. Dutt, L. Ericsson, P. Sanchez, S. A. Tsaftaris, and T. Hospedales, “Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity,” in Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning, N. Burgos, C. Petitjean, M. Vakalopoulou, S. Christodoulidis, P. Coupe, H. Delingette, C. Lartizien, and D. Mateus, Eds., in Proceedings of Machine Learning Research, vol. 250. PMLR, Oct. 2024, pp. 406–425.
M. Fischer, A. Bartler, and B. Yang, “Prompt tuning for parameter-efficient medical image segmentation,” Med. Image Anal., vol. 91, p. 103024, 2024. https://doi.org/10.1016/j.media.2023.103024
Y. Peng, “Efficient Deep Learning Methods for Medical Image Analysis,” Oct. 2024. https://doi.org/10.7274/27147567.v1
C. and K. M. and P. G. Loddo Andrea and Di Ruberto, “MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis,” in Processing and Analysis of Biomedical Information, J. and R. E. and R. D. and J. L. Lepore Natasha and Brieva, Ed., Cham: Springer International Publishing, 2019, pp. 57–65. https://doi.org/10.1007/978-3-030-13835-6_7
S. S. Abbas and T. M. H. Dijkstra, “Malaria-Detection-2019,” 2019, Mendeley Data. https://doi.org/10.17632/5bf2kmwvfn.1
F. Garcea, A. Serra, F. Lamberti, and L. Morra, “Data augmentation for medical imaging: A systematic literature review,” Comput. Biol. Med., vol. 152, p. 106391, 2023. https://doi.org/10.1016/j.compbiomed.2022.106391
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243
M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning, K. Chaudhuri and R. Salakhutdinov, Eds., in Proceedings of Machine Learning Research, vol. 97. PMLR, Oct. 2019, pp. 6105–6114.
J. Cheng et al., “ResGANet: Residual group attention network for medical image classification and segmentation,” Med. Image Anal., vol. 76, p. 102313, 2022. https://doi.org/10.1016/j.media.2021.102313
J. Snell, K. Swersky, and R. Zemel, “Prototypical Networks for Few-shot Learning,” in Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., Curran Associates, Inc., 2017.
K. Hoyos and W. Hoyos, “Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation,” Diagnostics, vol. 14, no. 7, 2024. https://doi.org/10.3390/diagnostics14070690
Y. Lei, R. L. J. Qiu, T. Wang, W. J. Curran, T. Liu, and X. Yang, “Chapter 7 - Generative adversarial networks for medical image synthesis,” in Biomedical Image Synthesis and Simulation, N. Burgos and D. Svoboda, Eds., Academic Press, 2022, pp. 105–128. https://doi.org/10.1016/B978-0-12-824349-7.00014-1
A. Kazerouni et al., “Diffusion models in medical imaging: A comprehensive survey,” Med. Image Anal., vol. 88, p. 102846, 2023. https://doi.org/10.1016/j.media.2023.102846
X. Fang, C. F. Chong, K. L. Wong, M. Simões, and B. K. Ng, “Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification,” Sci. Rep., vol. 15, no. 1, p. 2146, 2025. https://doi.org/10.1038/s41598-024-84836-9
A. Ouahab and O. Ben Ahmed, “ProtoMed: Prototypical networks with auxiliary regularization for few-shot medical image classification,” Image Vis. Comput., vol. 154, p. 105337, 2025. https://doi.org/10.1016/j.imavis.2024.105337
J. Zhang et al., “Advances in attention mechanisms for medical image segmentation,” Comput. Sci. Rev., vol. 56, p. 100721, 2025. https://doi.org/10.1016/j.cosrev.2024.100721
T. A. Aris, A. S. A. Nasir, W. A. Mustafa, M. Y. Mashor, E. V. Haryanto, and Z. Mohamed, “Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images,” Diagnostics, vol. 13, no. 3, 2023. https://doi.org/10.3390/diagnostics13030511
M. P. Singh et al., “A Healthcare System Employing Lightweight CNN for Disease Prediction with Artificial Intelligence,” Open Public Health J., vol. 17, no. 1, Jul. 2024. https://doi.org/10.2174/0118749445302023240520111802