Automated Malaria Detection Using Convolutional Neural Networks and Machine Learning

Adel Lateef Albukhnefis

doi:10.31185/wjps.576

Authors

Adel Lateef Albukhnefis University of Al-Qadisiyah, College of Computer Science and I. T.

DOI:

https://doi.org/10.31185/wjps.576

Abstract

ABSTRACT: Millions of people suffer from malaria, considered one of the most dangerous parasitic diseases threatening human life, especially in tropical and subtropical regions. There are many challenges in using traditional diagnostic methods such as blood smear checking, which can be achieved by using a microscope due to the inaccuracy of manual analysis and reliance on individual skills. Therefore, utilizing machine learning or deep learning algorithms for automating malaria detection provides encouraging solutions to enhance accuracy, minimize diagnostic time, and empower scalability. This paper employs a Convolutional Neural Network for automating malaria detection and classification using a dataset from the National Institute of Health (NIH) that is available publicly. The proposed model achieves an accuracy of 97.5%, and excellent results are performed regarding sensitivity and specificity when compared with machine learning algorithms such as Support Vector Machine (SVM) and Decision Tree. Furthermore, the results are validated using cross-validation techniques and compared with the existing methods. Our proposed CNN model can be deployed and potentially helps professionals with real-time malaria diagnosis and classification.

References

REFERENCES

World Health Organization. (2021). World malaria report 2021. Geneva: World Health Organization.

Ashley, E. A., Pyae Phyo, A., & Woodrow, C. J. (2018). Malaria. The Lancet, 391(10130), 1608-1621.

White, N. J., Pukrittayakamee, S., Hien, T. T., Faiz, M. A., Mokuolu, O. A., & Dondorp, A. M. (2014). Malaria. The Lancet, 383(9918), 723-735.

Tangpukdee, N., Duangdee, C., Wilairatana, P., & Krudsood, S. (2009). Malaria diagnosis: a brief review. The Korean journal of parasitology, 47(2), 93.

Wongsrichanalai, C., Barcus, M. J., Muth, S., Sutamihardja, A., & Wernsdorfer, W. H. (2007). A review of malaria diagnostic tools: microscopy and rapid diagnostic test (RDT). The American journal of tropical medicine and hygiene, 77(6_Suppl), 119-127.

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.

Mouatcho, J. C., & Goldring, J. D. (2013). Malaria rapid diagnostic tests: challenges and prospects. Journal of medical microbiology, 62(10), 1491-1505.

Das, D. K., Ghosh, M., Pal, M., Maiti, A. K., & Chakraborty, C. (2020). Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron, 130, 102800.

Rajaraman, S., Antani, S. K., Poostchi, M., Silamut, K., Hossain, M. A., Maude, R. J., ... & Thoma, G. R. (2018). Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ, 6, e4568.

Oliveira, A. D., Prats, C., Espasa, M., Zarzuela Serrat, F., Montañola Sales, C., Silgado, A., ... & Ginovart, M. (2017). The malaria system microApp: a new, mobile device-based tool for malaria diagnosis. JMIR research protocols, 6(4), e70.

Liang, Z., Powell, A., Ersoy, I., Poostchi, M., Silamut, K., Palaniappan, K., ... & Antani, S. (2016, December). CNN-based image analysis for malaria diagnosis. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 493-496). IEEE.

Diaz, G., Gonzalez, F. A., & Romero, E. (2009). A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of biomedical informatics, 42(2), 296-307.

G. Litjens et al., "A survey on deep learning in medical image analysis," Medical image analysis, vol. 42, pp. 60-88, 2017.

D. Shen, G. Wu, and H. I. Suk, "Deep learning in medical image analysis," Annual review of biomedical engineering, vol. 19, pp. 221-248, 2017.

Y. Dong et al., "Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells," in 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017, pp. 101-104.

S. Rajaraman et al., "Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images," PeerJ, vol. 6, p. e4568, 2018.

Z. Liang et al., "CNN-based image analysis for malaria diagnosis," in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2016, pp. 493-496.

R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into imaging, vol. 9, no. 4, pp. 611-629, 2018.

P. Rajpurkar et al., "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning," arXiv preprint arXiv:1711.05225, 2017.

J. Ker, L. Wang, J. Rao, and T. Lim, "Deep learning applications in medical image analysis," IEEE Access, vol. 6, pp. 9375-9389, 2018.

A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, pp. 115-118, 2017.

O. Faust, Y. Hagiwara, T. J. Hong, O. S. Lih, and U. R. Acharya, "Deep learning for healthcare applications based on physiological signals: A review," Computer methods and programs in biomedicine, vol. 161, pp. 1-13, 2018.

Kaustubh Chakradeo, Michael Delves, Sofya Titarenko, July 15, 2020, "Malaria dataset (thin blood smear, updated)", IEEE Dataport, doi: https://dx.doi.org/10.21227/mrgx-rh96.

H. S. Kim et al., "Data Augmentation Using Deep Generative Models in Medical Image Analysis: A Review," IEEE Reviews in Biomedical Engineering, vol. 15, pp. 215-237, 2022.

A. Lateef Albukhnefis, “An Efficient Algorithm for Automated Skin Lesion Detection: A Non-Machine Learning Approach”, J. Al-Qadisiyah Comp. Sci. Math., vol. 16, no. 3, pp. Comp Page 1–11, Sep. 2024.