This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Parameter Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images
Corresponding Author(s) : Akhiyar Waladi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026 (Article in Progress)
Abstract
Malaria diagnostic automation faced critical challenges including severe class imbalance with ratios up to 54:1, limited datasets with 200 to 500 images, and computational inefficiency requiring separate model training for each detection-classification combination. This study developed a multi-model framework with shared classification architecture that trained classification models once on ground truth crops and reused 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 70.84% to 96.27% mAP@50 with high recall of 71.05% to 93.12% minimizing missed parasites. Classification demonstrated 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 91.51% accuracy on IML Lifecycle and 98.28% on MP-IDB Species, while EfficientNet-B0 achieved 86.45% on multi-patient MD-2019 dataset. ResNet50 achieved 96.13% on severely imbalanced MP-IDB Stages. Focal Loss optimization with alpha of 1.0 and gamma of 1.5 enabled robust minority class performance with F1-scores between 0.44 and 1.00 on ultra-minority classes demonstrating effective imbalance handling. The compact 46-89 MB models enabled 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. [Online]. Available: https://iris.who.int/handle/10665/379751
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References
W. H. Organization, World malaria report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization, 2024. [Online]. Available: https://iris.who.int/handle/10665/379751
World Health Organization, “Global technical strategy for malaria 2016–2030, 2021 update,” 2021. Accessed: May 23, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789240031357
H. Sutanto, “Combating Malaria with Vaccines: Insights from the One Health Framework,” Sep. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 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, doi: 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, doi: 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, doi: 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. doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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. [Online]. Available: https://proceedings.mlr.press/v250/dutt24a.html
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Y. Peng, “Efficient Deep Learning Methods for Medical Image Analysis,” Oct. 2024, doi: 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.
S. S. Abbas and T. M. H. Dijkstra, “Malaria-Detection-2019,” 2019, Mendeley Data. doi: 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, doi: https://doi.org/10.1016/j.compbiomed.2022.106391.
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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. [Online]. Available: https://proceedings.mlr.press/v97/tan19a.html
J. Cheng et al., “ResGANet: Residual group attention network for medical image classification and segmentation,” Med. Image Anal., vol. 76, p. 102313, 2022, doi: 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. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/cb8da6767461f2812ae4290eac7cbc42-Paper.pdf
K. Hoyos and W. Hoyos, “Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation,” Diagnostics, vol. 14, no. 7, 2024, doi: 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. doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 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, doi: 10.2174/0118749445302023240520111802.