Multi-Architecture CNN Analysis for Automated Malaria Parasite Classification on MP-IDB Dataset
DOI:
https://doi.org/10.22437/proca.v2i2.55514Keywords:
Malaria Detection, Deep Learning, YOLO, Class Imbalance, Medical Imaging, Plasmodium Species, Lifecycle StagesAbstract
Malaria remains a critical global health challenge with over 200 million annual cases, yet traditional microscopic diagnosis is time-consuming and requires expert pathologists. This study proposes a multi-model hybrid framework combining YOLO (v10-v12) for detection and six CNN architectures (DenseNet121, EfficientNet-B0/B1/B2, ResNet50/101) for classification, validated on two public MP-IDB datasets comprising 418 images across 8 classes (4 Plasmodium species and 4 lifecycle stages). The proposed shared classification architecture generates ground truth crops once and reuses them across all detection methods, enabling efficient model training. YOLOv11 achieves 93.10% mAP@50 with 92.26% recall on species detection and 92.90% mAP@50 with 90.37% recall on lifecycle stages. For classification, EfficientNet-B1 reaches 98.80% accuracy (93.18% balanced accuracy) on species despite 45:1 class imbalance, while EfficientNet-B0 achieves 94.31% accuracy (69.21% balanced accuracy) on lifecycle stages with 54:1 imbalance. A notable finding is that smaller EfficientNet models (5.3-7.8M parameters) outperform larger ResNet variants (25.6-44.5M parameters), with EfficientNet-B1 achieving 10.5 percentage points higher balanced accuracy than ResNet101 (93.18% vs 82.73%) despite 5.7× fewer parameters, challenging the "deeper is better" paradigm. The system addresses extreme class imbalance through Focal Loss (α=0.25, γ=2.0), achieving minority class F1-scores of 76.9% on P. ovale with perfect recall and 92.3% on schizont, though trophozoite (51.6%) and gametocyte (57.1%) remain challenging. Implemented on consumer-grade GPU hardware, the framework demonstrates practical feasibility for deployment in resource-constrained settings, though clinical use requires validation on larger datasets and field-collected samples.
References
1. World Health Organization, World Malaria Report 2024, Geneva, Switzerland, 2024.
2. Snow RW et al. The global distribution of clinical episodes of Plasmodium falciparum malaria, Nature. 2005;434:214-217.
3. Centers for Disease Control and Prevention, Malaria Biology, 2024. Available from: https://www.cdc.gov/malaria/about/biology/.
4. Moody A. Rapid diagnostic tests for malaria parasites, Clin. Microbiol. Rev.. 2002;15(1):66-78.
5. WHO, Malaria Microscopy Quality Assurance Manual, ver. 2.0, Geneva, 2016.
6. Chiodini PL et al. Manson's Tropical Diseases, 23rd ed. London: Elsevier, 2014, ch. 52.
7. O'Meara J et al. Sources of variability in determining malaria parasite density by microscopy, Am. J. Trop. Med. Hyg.. 2005;73(3):593-598.
8. Mitsakakis K et al. Challenges in malaria diagnosis, Expert Rev. Mol. Diagn.. 2018;18(10):867-875.
9. Esteva A et al. Dermatologist-level classification of skin cancer with deep neural networks, Nature. 2017;542:115-118.
10. Rajpurkar P et al. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning, arXiv:1711.05225, 2017.
11. Coudray N et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning, Nat. Med.. 2018;24:1559-1567.
12. Rajaraman S et al. Pre-trained convolutional neural networks as feature extractors for diagnosis of malaria from blood smears, Diagnostics. 2018;8(4):74.
13. Wang A et al. YOLOv10: Real-time end-to-end object detection, arXiv:2405.14458, 2024.
14. Jocher G et al. YOLOv11: Ultralytics YOLO11, 2024. Available from: https://github.com/ultralytics/ultralytics.
15. Poostchi F et al. Image analysis and machine learning for detecting malaria, Transl. Res.. 2018;194:36-55.
16. Rosenthal P. How do we diagnose and treat Plasmodium ovale and Plasmodium malariae? Curr. Infect. Dis. Rep.. 2008;10:58-61.
17. Ren S et al. Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell.. 2017;39(6):1137-1149.
18. WHO, Basic Malaria Microscopy: Part I. Learner's guide, 2nd ed., Geneva, 2010.
19. Huang G et al. Densely connected convolutional networks, in Proc. IEEE CVPR, 2017, pp. 4700-4708.
20. M. Tan and Q. V. Le, EfficientNet: Rethinking model scaling for convolutional neural networks, in Proc. ICML, 2019, pp. 6105-6114.
21. He K et al. Deep residual learning for image recognition, in Proc. IEEE CVPR, 2016, pp. 770-778.
22. T.-Y. Lin et al., Focal loss for dense object detection, IEEE Trans. Pattern Anal. Mach. Intell.. 2020;42(2):318-327.
23. Aikawa M. Parasitological review: Plasmodium, Exp. Parasitol.. 1971;30(2):284-320.
24. A. Vijayalakshmi and B. Rajesh Kanna, Deep learning approach to detect malaria from microscopic images, Multim. Tools Appl.. 2020;79:15297-15317.
25. Deng J et al. ImageNet: A large-scale hierarchical image database, in Proc. IEEE CVPR, 2009, pp. 248-255.
26. Dosovitskiy A et al. An image is worth 16×16 words: Transformers for image recognition at scale, in Proc. ICLR, 2021.
27. Goodfellow I et al. Generative adversarial nets, in Proc. NeurIPS, 2014, pp. 2672-2680.
28. Ho J et al. Denoising diffusion probabilistic models, in Proc. NeurIPS, 2020.
29. Settles B. Active learning literature survey, Univ. Wisconsin-Madison, Tech. Rep. 1648, 2009.
30. Finn C et al. Model-agnostic meta-learning for fast adaptation of deep networks, in Proc. ICML, 2017, pp. 1126-1135.
31. WHO, Guidelines for the Treatment of Malaria, 3rd ed., Geneva, 2015.
32. Long CJ et al. A smartphone-based portable biosensor for diagnosis in resource-limited settings, Nature Biotechnol.. 2014;32:373-379.
33. Krishnamoorthi R. Quantizing deep convolutional networks for efficient inference, arXiv:1806.08342, 2018.
34. Han S et al. Learning both weights and connections for efficient neural network, in Proc. NeurIPS, 2015, pp. 1135-1143.
35. FDA, Clinical decision support software: Guidance for industry and FDA staff, 2022.
36. Zhang H et al. mixup: Beyond empirical risk minimization, in Proc. ICLR, 2018.
37. Vinyals O et al. Matching networks for one shot learning, in Proc. NeurIPS, 2016, pp. 3630-3638.
38. Ganin Y et al. Domain-adversarial training of neural networks, J. Mach. Learn. Res.. 2016;17(1):2096-2030.
39. Kirillov A et al. Segment anything, in Proc. IEEE ICCV, 2023, pp. 4015-4026.
40. Selvaraju RR et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization, Int. J. Comput. Vis.. 2020;128:336-359.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Akhiyar Waladi, Nindy Raisa Hanum, Yogi Perdana, Hasanatul Iftitah, Fitra Wahyuni, Rahmad Ashar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Published with license by LPPM Universitas Jambi. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0 International). This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator.
