CFP last date
20 August 2025
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2025

Submit your paper
Know more
The week's pick
Responsive image
Assessing LLMs as Cognitive Interpreters of Student Prompts: A Typological Framework

Tadeu da Ponte Matevz Vremec Matej Mertik
Random Articles
Reseach Article

Advances in Malaria Detection: Synergizing Deep Learning and Traditional Machine Learning

by Samuel Yao Sebuabe, Justice Kwame Appati, Stephen Kofi Dotse, John Yao Akakpo, Doe Martin
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 14
Year of Publication: 2025
Authors: Samuel Yao Sebuabe, Justice Kwame Appati, Stephen Kofi Dotse, John Yao Akakpo, Doe Martin
10.5120/ijca2025925171

Samuel Yao Sebuabe, Justice Kwame Appati, Stephen Kofi Dotse, John Yao Akakpo, Doe Martin . Advances in Malaria Detection: Synergizing Deep Learning and Traditional Machine Learning. International Journal of Computer Applications. 187, 14 ( Jun 2025), 53-65. DOI=10.5120/ijca2025925171

@article{ 10.5120/ijca2025925171,
author = { Samuel Yao Sebuabe, Justice Kwame Appati, Stephen Kofi Dotse, John Yao Akakpo, Doe Martin },
title = { Advances in Malaria Detection: Synergizing Deep Learning and Traditional Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2025 },
volume = { 187 },
number = { 14 },
month = { Jun },
year = { 2025 },
issn = { 0975-8887 },
pages = { 53-65 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number14/advances-in-malaria-detection-synergizing-deep-learning-and-traditional-machine-learning/ },
doi = { 10.5120/ijca2025925171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-06-26T19:04:43.389923+05:30
%A Samuel Yao Sebuabe
%A Justice Kwame Appati
%A Stephen Kofi Dotse
%A John Yao Akakpo
%A Doe Martin
%T Advances in Malaria Detection: Synergizing Deep Learning and Traditional Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 14
%P 53-65
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Malaria remains one of the leading causes of death globally, necessitating continuous research into novel diagnostic and treatment methods. Despite available treatments, accurately assessing drug efficacy against malaria parasites remains challenging due to the need for precise parasite quantification in blood-smeared images, a task traditionally performed using time-consuming microscopy methods. In this study, we propose a Convolutional Neural Network (CNN)-based deep learning model to enhance malaria parasite detection from Giemsa-stained thin blood smears. The proposed model incorporates advanced preprocessing techniques, including normalization, standardization, and staining, as well as data augmentation methods (e.g., random cropping, flipping, and rotation) and hyperparameter optimization to significantly improve performance. The primary dataset from the National Institutes of Health (NIH), consisting of 27,558 parasitized and uninfected cell images, was used to train and evaluate the model. A custom CNN architecture was compared with pre-trained models like VGG-19, ResNet-50, and MobileNetV2 based on accuracy, precision, recall and AUC. The best-performing model achieved a training accuracy of 96.88%, validation accuracy of 95.55%, and test accuracy of 95.67% after 50 epochs. Performance metrics such as precision (97.37%), recall (97.75%), and AUC (99.19%) demonstrated high sensitivity and specificity, confirming the model’s robustness. A secondary dataset from the IEEE repository, containing 43,434 images, was used to validate the model, yielding near-identical performance and further confirming its generalizability across diverse datasets. These findings underscore the proposed model’s ability to accurately detect malaria parasites, offering a faster and more reliable alternative to traditional microscopy. Future work will explore integrating mobile-based imaging systems with cloud and edge-based inference for deployment in low-resource settings, aiming to enhance malaria treatment outcomes in underserved regions.

References
  1. World Malaria Report 2023. World Health Organization, 2023.
  2. E. Guemas et al., “Automatic patient-level recognition of four Plasmodium species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation,” Microbiol Spectr, vol. 12, no. 2, Feb. 2024, doi: 10.1128/spectrum.01440-23.
  3. L. Zedda, A. Loddo, and C. Di Ruberto, “A deep architecture based on attention mechanisms for effective end-to-end detection of early and mature malaria parasites,” Biomed Signal Process Control, vol. 94, Aug. 2024, doi: 10.1016/j.bspc.2024.106289.
  4. E. Hassan, M. Y. Shams, N. A. Hikal, and S. Elmougy, “A Novel Convolutional Neural Network Model for Malaria Cell Images Classification,” Computers, Materials and Continua, vol. 72, no. 3, pp. 5889–5907, 2022, doi: 10.32604/cmc.2022.025629.
  5. A. Singh, M. Mehra, A. Kumar, M. Niranjannaik, D. Priya, and K. Gaurav, “Leveraging hybrid machine learning and data fusion for accurate mapping of malaria cases using meteorological variables in western India,” Intelligent Systems with Applications, vol. 17, Feb. 2023, doi: 10.1016/j.iswa.2022.200164.
  6. P. A. Pattanaik, M. Mittal, M. Z. Khan, and S. N. Panda, “Malaria detection using deep residual networks with mobile microscopy,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1700–1705, May 2022, doi: 10.1016/j.jksuci.2020.07.003.
  7. M. Bhuiyan and M. S. Islam, “A new ensemble learning approach to detect malaria from microscopic red blood cell images,” Sensors International, vol. 4, Jan. 2023, doi: 10.1016/j.sintl.2022.100209.
  8. A. Alassaf and M. Y. Sikkandar, “Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image,” Computers, Materials and Continua, vol. 72, no. 3, pp. 5273–5285, 2022, doi: 10.32604/cmc.2022.025577.
  9. I. Amin, S. Hassan, S. B. Belhaouari, and M. H. Azam, “Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification,” Computers, Materials and Continua, vol. 74, no. 3, pp. 6335–6349, 2023, doi: 10.32604/cmc.2023.033860.
  10. T. K. Kundu, D. K. Anguraj, and S. V. Sudha, “Modeling a Novel Hyper-Parameter Tuned Deep Learning EnabledMalaria Parasite Detection and Classification,” Computers, Materials and Continua, vol. 77, no. 3, pp. 3289–3304, 2023, doi: 10.32604/cmc.2023.039515.
  11. A. Koirala et al., “Deep Learning for Real-Time Malaria Parasite Detection and Counting Using YOLO-mp,” IEEE Access, vol. 10, pp. 102157–102172, 2022, doi: 10.1109/ACCESS.2022.3208270.
  12. D. Crossed D Signumic, D. Keco, and Z. Mašetic, “Automatization of Microscopy Malaria Diagnosis Using Computer Vision and Random Forest Method,” in IFAC-PapersOnLine, Elsevier B.V., 2022, pp. 80–84. doi: 10.1016/j.ifacol.2022.06.013.
  13. I. Jdey, G. Hcini, and H. Ltifi, “Deep learning and machine learning for Malaria detection: overview, challenges and future directions,” Sep. 2022, [Online]. Available: http://arxiv.org/abs/2209.13292
  14. Y. Alraba’nah and W. Toghuj, “A deep learning based architecture for malaria parasite detection,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 292–299, Feb. 2024, doi: 10.11591/eei.v13i1.5485.
  15. O. S. Zhao et al., “Convolutional neural networks to automate the screening of malaria in low-resource countries,” PeerJ, vol. 8, 2020, doi: 10.7717/peerj.9674.
  16. “Convolutional Neural Network: A Complete Guide.” Accessed: Dec. 20, 2024. [Online]. Available: https://learnopencv.com/understanding-convolutional-neural-networks-cnn/
  17. S. Shambhu, D. Koundal, P. Das, V. T. Hoang, K. Tran-Trung, and H. Turabieh, “Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/3626726.
  18. E. Prasetyo, R. Purbaningtyas, R. D. Adityo, N. Suciati, and C. Fatichah, “Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes,” Information Processing in Agriculture, vol. 9, no. 4, pp. 485–496, Dec. 2022, doi: 10.1016/j.inpa.2022.01.002.
  19. D. T. Rademaker et al., “Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells,” iScience, vol. 26, no. 12, Dec. 2023, doi: 10.1016/j.isci.2023.108542.
  20. S. Sawant and A. Singh, “Malaria Cell Detection Using Deep Neural Networks,” Jun. 2024, [Online]. Available: http://arxiv.org/abs/2406.20005
  21. M. Mujahid et al., “Efficient deep learning-based approach for malaria detection using red blood cell smears,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-63831-0.
  22. “Malaria Datasheet.” Accessed: Dec. 20, 2024. [Online]. Available: https://lhncbc.nlm.nih.gov/LHC-research/LHC-projects/image-processing/malaria-datasheet.html
  23. F. Yang et al., “Cascading YOLO: automated malaria parasite detection for Plasmodium vivax in thin blood smears,” p. 58, Mar. 2020, doi: 10.1117/12.2549701.
  24. “Malaria Thick Blood Smears | IEEE DataPort.” Accessed: Dec. 20, 2024. [Online]. Available: https://ieee-dataport.org/documents/malaria-thick-blood-smears
  25. 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, May 2020, doi: 10.1109/JBHI.2019.2939121.
  26. K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/J.GLTP.2022.04.020.
  27. “Image Filtering Techniques in Image Processing.” Accessed: Dec. 20, 2024. [Online]. Available: https://www.imageprovision.com/articles/understanding-image-filtering-techniques-in-image-processing.
  28. P. V. Dantas, W. Sabino da Silva, L. C. Cordeiro, and C. B. Carvalho, “A comprehensive review of model compression techniques in machine learning,” Applied Intelligence 2024 54:22, vol. 54, no. 22, pp. 11804–11844, Sep. 2024, doi: 10.1007/S10489-024-05747-W.
  29. M. Mafi, H. Martin, M. Cabrerizo, J. Andrian, A. Barreto, and M. Adjouadi, “A comprehensive survey on impulse and Gaussian denoising filters for digital images,” Signal Processing, vol. 157, pp. 236–260, Apr. 2019, doi: 10.1016/J.SIGPRO.2018.12.006.
  30. S. Elmi and Z. Elmi, “A robust edge detection technique based on Matching Pursuit algorithm for natural and medical images,” Biomedical Engineering Advances, vol. 4, p. 100052, Dec. 2022, doi: 10.1016/J.BEA.2022.100052.
  31. X. Li, “Image Texture Analysis and Edge Detection Algorithm Based on Anisotropic Diffusion Equation,” Advances in Mathematical Physics, vol. 2021, no. 1, p. 9910882, Jan. 2021, doi: 10.1155/2021/9910882.
  32. K. Muntarina, R. Mostafiz, F. Khanom, S. B. Shorif, and M. S. Uddin, “MultiResEdge: A deep learning-based edge detection approach,” Intelligent Systems with Applications, vol. 20, Nov. 2023, doi: 10.1016/j.iswa.2023.200274.
  33. S. S. Bao, Y. R. Huang, J. C. Xu, and G. Y. Xu, “Pixel Difference Unmixing Feature Networks for Edge Detection,” IEEE Access, vol. 11, pp. 52370–52380, 2023, doi: 10.1109/ACCESS.2023.3279276.
  34. S. Seoni et al., “All you need is data preparation: A systematic review of image harmonization techniques in multi-center/device studies for medical support systems,” Comput Methods Programs Biomed, vol. 250, p. 108200, Jun. 2024, doi: 10.1016/J.CMPB.2024.108200.
  35. M. Salvi, F. Branciforti, F. Molinari, and K. M. Meiburger, “Generative models for color normalization in digital pathology and dermatology: Advancing the learning paradigm,” Expert Syst Appl, vol. 245, p. 123105, Jul. 2024, doi: 10.1016/J.ESWA.2023.123105.
  36. N. Schiess, A. Villabona-Rueda, K. E. Cottier, K. Huether, J. Chipeta, and M. F. Stins, “Pathophysiology and neurologic sequelae of cerebral malaria,” Malar J, vol. 19, no. 1, Jul. 2020, doi: 10.1186/S12936-020-03336-Z.
  37. A. M. Qadri, A. Raza, F. Eid, and L. Abualigah, “A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images,” Decision Analytics Journal, vol. 9, p. 100352, Dec. 2023, doi: 10.1016/J.DAJOUR.2023.100352.
  38. M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” Computers 2023, Vol. 12, Page 91, vol. 12, no. 5, p. 91, Apr. 2023, doi: 10.3390/COMPUTERS12050091.
Index Terms

Computer Science
Information Sciences

Keywords

Malaria Convolutional Neural Network Cell Images Minutiae IEEE Dataset Giemsa-stained Thin Blood Smears

Powered by PhDFocusTM