GEOMETRIC FEATURES: A CRITICAL COMPONENT FOR ACCURATE MALARIA PARASITE STAGE CLASSIFICATION IN THIN SMEAR MICROSCOPY
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.4332Keywords:
Geometric Features, Malaria Diagnosis, Random Forest Classifier, XGBoost, MLP Classifier, Multiclass ClassificationAbstract [English]
Malaria-a global health problem always demands an accurate and timely diagnosis of a disease for its proper treatment. Traditional methods like microscopic examination are time-consuming and require specialized expertise. It thus poses challenges in resource-limited areas. Automated classification of malaria parasite stages helps in improving the diagnostic efficiency. In this paper, the importance of geometric features in malaria parasite stage classification using machine learning techniques has been realized. Geometric features, including area, perimeter, and shape descriptors, offer valuable information regarding the morphological differences between the various stages of the parasite. We compare the performance of the following machine learning models using geometric features: Random Forest, GaussianNB, XGBoost, and MLPClassifier. The results show that the inclusion of geometric features improves the accuracy and robustness of the machine learning models for classification. Among the different models tested in this study, MLP Classifier had 95.90% accuracy thus shows tremendous potential for a geometric feature in a malaria diagnosis program. This current study, therefore, gives way to advancement in automated diagnosis of malaria among others and further pursuit of geometric-based applications in their fields.
References
Abbas, S. S., & Dijkstra, T. M. H. (2020a). Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers. Diagnostic Pathology, 15(1), 130. https://doi.org/10.1186/s13000-020-01040-9
Abbas, S. S., & Dijkstra, T. M. H. (2020b). Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers. Diagnostic Pathology, 15(1), 130. https://doi.org/10.1186/s13000-020-01040-9 DOI: https://doi.org/10.1186/s13000-020-01040-9
Abdurahman, F., Fante, K. A., & Aliy, M. (2021). Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models. BMC Bioinformatics, 22(1), 112. https://doi.org/10.1186/s12859-021-04036-4 DOI: https://doi.org/10.1186/s12859-021-04036-4
Akcakır, O., Celebi, L. K., Kamil, M., & Aly, A. S. I. (2022). Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears. Biomedical Optics Express, 13(7), 3904. https://doi.org/10.1364/BOE.448099 DOI: https://doi.org/10.1364/BOE.448099
Aris, T., Nasir, A., Mustafa, W., Mashor, M., Haryanto, E., & Mohamed, Z. (2023). Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images. Diagnostics, 13(3), 511. https://doi.org/10.3390/diagnostics13030511 DOI: https://doi.org/10.3390/diagnostics13030511
Bashar, Md. K. (2019). Improved Classification of Malaria Parasite Stages with Support Vector Machine Using Combined Color and Texture Features. 2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT), 135–138. https://doi.org/10.1109/HI-POCT45284.2019.8962686 DOI: https://doi.org/10.1109/HI-POCT45284.2019.8962686
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 DOI: https://doi.org/10.1145/2939672.2939785
Heide, J., Vaughan, K. C., Sette, A., Jacobs, T., & Schulze Zur Wiesch, J. (2019). Comprehensive Review of Human Plasmodium falciparum-Specific CD8+ T Cell Epitopes. Frontiers in Immunology, 10, 397. https://doi.org/10.3389/fimmu.2019.00397 DOI: https://doi.org/10.3389/fimmu.2019.00397
Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y., Wang, L., & Cao, D. (2021). The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis. Engineering, 7(6), 845–856. https://doi.org/10.1016/j.eng.2020.07.030 DOI: https://doi.org/10.1016/j.eng.2020.07.030
Loddo, A., Di Ruberto, C., & Kocher, M. (2018). Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology. Sensors, 18(2), 513. https://doi.org/10.3390/s18020513 DOI: https://doi.org/10.3390/s18020513
Loddo, A., Di Ruberto, C., Kocher, M., & Prod’Hom, G. (2019). MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis. In N. Lepore, J. Brieva, E. Romero, D. Racoceanu, & L. Joskowicz (Eds.), Processing and Analysis of Biomedical Information (Vol. 11379, pp. 57–65). Springer International Publishing. https://doi.org/10.1007/978-3-030-13835-6_7 DOI: https://doi.org/10.1007/978-3-030-13835-6_7
Nugroho, A. S., Winarta, T., Wibisono, Y., Galinium, M., Rozi, I. E., & Asih, P. B. S. (2020). Morpho-Geometrical Feature Extraction of Thin Blood Smear Microphotograph for Malaria Plasmodia Species and Life Stage Determination. 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 95–100. https://doi.org/10.1109/ICACSIS51025.2020.9263220 DOI: https://doi.org/10.1109/ICACSIS51025.2020.9263220
Nugroho, H. A., Akbar, S. A., & Murhandarwati, E. E. H. (2015). Feature extraction and classification for detection malaria parasites in thin blood smear. 2015 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 197–201. https://doi.org/10.1109/ICITACEE.2015.7437798 DOI: https://doi.org/10.1109/ICITACEE.2015.7437798
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/10.1016/j.trsl.2017.12.004 DOI: https://doi.org/10.1016/j.trsl.2017.12.004
Pushpakumar, R., Prabu, R., Priscilla, M., Renisha, P. S., Prabu, R. T., & Muthuraman, U. (2022). A Novel Approach to Identify Dynamic Deficiency in Cell using Gaussian NB Classifier. 2022 7th International Conference on Communication and Electronics Systems (ICCES), 31–37. https://doi.org/10.1109/ICCES54183.2022.9835813 DOI: https://doi.org/10.1109/ICCES54183.2022.9835813
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. International Journal of Computer Applications, 17(8), 43–48. https://doi.org/10.5120/2237-2860 DOI: https://doi.org/10.5120/2237-2860
Sora-Cardenas, J., Fong-Amaris, W. M., Salazar-Centeno, C. A., Castañeda, A., Martínez-Bernal, O. D., Suárez, D. R., & Martínez, C. (2025). Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears. Sensors, 25(2), 390. https://doi.org/10.3390/s25020390 DOI: https://doi.org/10.3390/s25020390
Talapko, J., Škrlec, I., Alebić, T., Jukić, M., & Včev, A. (2019). Malaria: The Past and the Present. Microorganisms, 7(6), 179. https://doi.org/10.3390/microorganisms7060179 DOI: https://doi.org/10.3390/microorganisms7060179
Tek, F. B., Dempster, A. G., & Kale, İ. (2010). Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding, 114(1), 21–32. https://doi.org/10.1016/j.cviu.2009.08.003 DOI: https://doi.org/10.1016/j.cviu.2009.08.003
Tin Kam Ho. (1995). Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition, 1, 278–282. https://doi.org/10.1109/ICDAR.1995.598994 DOI: https://doi.org/10.1109/ICDAR.1995.598994
Virk, A. S., Kaur, M., & Passrija, L. (2012). Performance Evaluation of Image Enhancement Techniques in Spatial and Wavelet Domains. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 3(1), 162–166. https://doi.org/10.24297/ijct.v3i1c.2771 DOI: https://doi.org/10.24297/ijct.v3i1c.2771
World Health Organization. (n.d.). Compendium of WHO malaria guidance: Prevention, diagnosis, treatment, surveillance and elimination Thumbnail V. World Health Organization. https://iris.who.int/handle/10665/312082
Zare, M., Pourghasemi, H. R., Vafakhah, M., & Pradhan, B. (2013). Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: A comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arabian Journal of Geosciences, 6(8), 2873–2888. https://doi.org/10.1007/s12517-012-0610-x DOI: https://doi.org/10.1007/s12517-012-0610-x
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