Development of a Mobile-Based Convolution Neural Network Framework for Rapid Malaria Detection from Blood Smears

  • Matthias O. Oladele Federal Polytechnic Ede
  • Temilola M. Adepoju Federal Polytechnic Ayede
  • Abdulahi A. Badrudeen Federal Polytechnic Ede
Keywords: Android smartphones, Blood smear images, Deep learning, Malaria parasite detection, Mobile-based framework

Abstract

Malaria, a vector-borne parasitic disease caused by Plasmodium parasites, continues to pose a substantial threat to global public health. Despite considerable progress in its control and treatment, malaria remains a significant cause of morbidity and mortality, particularly in resource-limited regions. The ability to diagnose malaria infections accurately and swiftly is paramount for effective disease management. Traditional methods of malaria diagnosis, relying on microscopic examination of blood smears, have proven to be accurate but are often hindered by limitations in infrastructure and expertise, particularly in remote and underserved areas. This research focuses on the development of a mobile-based Convolution Neural Network (CNN) framework for rapid malaria parasite detection from blood smear images using Android mobile phones.  Blood smear images provide a reasonable and clinically relevant representation of malaria disease which makes it a preferred choice for malaria detection from mobile phones. By leveraging smartphone technology and state-of-the-art deep learning algorithms, this pioneering endeavour aims to normalize malaria diagnostics, providing healthcare workers with a powerful tool for early detection and treatment, irrespective of their location or access to advanced laboratory facilities. The dataset for infected and non-infected parasites was acquired online from Kaggle. The dataset was preprocessed and partitioned into training, testing, and validation sets. The developed model was converted to tflite and was used for the mobile application. A validation accuracy of 94.88% was obtained from the validation set while an accuracy of 95.81% was obtained from the testing set.

References

Kundu, T. K., & Anguraj, D. K. (2023). A Performance Analysis of Machine Learning Algorithms for Malaria Parasite Detection using Microscopic Images. 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), 980–984. https://doi.org/10.1109/ICSSIT55814.2023.10061060

Kuzhaloli, S., Thenappan, S., P., T., Nivedita, V., Mageshbabu, M., & Navaneethan, S. (2023). Identification of Malaria Disease Using Machine Learning Models. 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 1–4. https://doi.org/10.1109/ICECCT56650.2023.10179665

Landier, J., Parker, D. M., Thu, A. M., Carrara, V. I., Lwin, K. M., Bonnington, C. A., Pukrittayakamee, S., Delmas, G., & Nosten, F. H. (2016). The role of early detection and treatment in malaria elimination. Malaria Journal, 15(1), 363. https://doi.org/10.1186/s12936-016-1399-y

Mahmood, S. N., Mohammed, S. S., Ismaeel, A. G., Clarke, H. G., Mahmood, I. N., Aziz, D. A., & Alani, S. (2023). Improved Malaria Cells Detection Using Deep Convolutional Neural Network. 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 1–4. https://doi.org/10.1109/HORA58378.2023.10156747

Mbanefo, A., & Kumar, N. (2020). Evaluation of Malaria Diagnostic Methods as a Key for Successful Control and Elimination Programs. Tropical Medicine and Infectious Disease, 5(2). https://doi.org/10.3390/tropicalmed5020102

Olugboja, A., & Wang, Z. (2017). Malaria parasite detection using different machine learning classifier. 2017 International Conference on Machine Learning and Cybernetics (ICMLC), 1, 246–250. https://doi.org/10.1109/ICMLC.2017.8107772

Paul, A., & Bania, R. K. (2021). Malaria Parasite Classification using Deep Convolutional Neural Network. 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), 1–6. https://doi.org/10.1109/ICCICA52458.2021.9697307

Poojary, H. D., & Sumithra, T. V. (2022). Comparative Analysis of Deep Learning Models for Malaria Detection. 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), 1–6. https://doi.org/10.1109/GCAT55367.2022.9972167

Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018). Image analysis and machine learning for detecting malaria. Translational Research, 194, 36–55. https://doi.org/https://doi.org/10.1016/j.trsl.2017.12.004

Raj, M., Sharma, R., & Sain, D. (2021). A Deep Convolutional Neural Network for Detection of Malaria Parasite in Thin Blood Smear Images. 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 510–514. https://doi.org/10.1109/CSNT51715.2021.9509619

Shah, D., Kawale, K., Shah, M., Randive, S., & Mapari, R. (2020). Malaria Parasite Detection Using Deep Learning : (Beneficial to humankind). 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 984–988. https://doi.org/10.1109/ICICCS48265.2020.9121073

Sifat, M. M. H., & Islam, M. M. (2020). A Fully Automated System to Detect Malaria Parasites and their Stages from the Blood Smear. 2020 IEEE Region 10 Symposium (TENSYMP), 1351–1354. https://doi.org/10.1109/TENSYMP50017.2020.9230761

WHO. (2023). Malaria. https://www.who.int/news-room/fact-sheets/detail/malaria

Yang, F., Poostchi, M., Yu, H., Zhou, Z., Silamut, K., Yu, J., Maude, R. J., Jaeger, S., & Antani, S. (2020). Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE Journal of Biomedical and Health Informatics, 24(5), 1427–1438. https://doi.org/10.1109/JBHI.2019.2939121

Published
2023-09-30