Detecting Malaria Cells with Plasmo-D Expert System Developed on Android and Computer Vision
DOI:
https://doi.org/10.26740/vubeta.v2i3.39626Keywords:
cell images, computer vision, Expert system, Plasmo-DAbstract
Separation between infected and uninfected cells during diagnosing malaria parasites plasmodium is difficult, time-consuming, and expensive. However, this article presented a report on a developed expert system called Plasmodium Detector(Plasmo-D), capable of differentiating plasmodium-infected and uninfected cells from malaria-infected patients. Plasmo-D was built on an Android application, with an information menu, splash, and classification screen, including an image recognition system that worked with computer vision. 27,528 cell images were collected online from the Data Library of the United States National Library of Medicine, containing infected and uninfected cells for training. No cell images were used as control. Plasmo-D fabrication and testing were conducted at the Instrumentation Laboratory, Department of Systems Engineering, University of Lagos, Nigeria. Studied parameters included cell images, backgrounds, visual style, size, type, lighting, and camera angle. Trained models were exported into an Android application through Java programming language and user interface through Android XML (Extensible Markup Language). Trained data results indicated that 99.8% desired level of accuracy was obtained after cell images were fed into the computer vision application programming interface. The trend was that Plasmo-D efficiency was higher for infected image cells, average for uninfected image cells and the least for no cell photo.
Author Biographies
Eganoosi Atojunere, Department of Soil Science and Biosystem Engineering, Fiji National University, Fiji.
Eganoosi Esme Atojunere is an Assistant Professor in the Department of Soil Science and Biosystem Engineering, Fiji National University, Fiji. He received B.Eng., M.Sc. and Ph.D. in Agricultural and Biosystem Engineering from the University of Ilorin and University of Ibadan Nigeria respectively. His research interest includes Artificial intelligence, System Engineering, Machinery design, Engineering management and Biomedical Engineering and Agricultural and Environmental Engineering. His email(s) are eganoosi.atojunere@fnu,ac,fj, atojunereeganoosi@yahoo.com.
Temilola. Adewunmi Onaneye, Department of Systems Engineering, University of Lagos, Akoka Yaba, Lagos, Nigeria.
Onaneye Adewunmi Temilola is a graduate student in the Department of System Engineering, University of Lagos, Nigeria. His main research area is Artificial intelligence, machine learning, software and app. system developer. He can be contacted through email: onaneyetemilola@gmail.com.
References
[1] World Health Organization X/sr0015 ,2022. http://refhub.elsevier.com/S1931-5244(17)30333
[2] WHO Determining cost effectiveness of malaria rapid diagnostic tests in rural areas with high prevalence.
[3] World Health Organization. Task shifting. Global recommendations and guidelines. WHO Press,Geneva, Switzerland. 2008.
[4] World Health Organization, Basic malaria microscopy. Second ed. WHO Press, World Health Organization. Geneva Switzerland, 2010
[5] N.D,Wolfe, A.A. Escalante, W.B. Karesh, A. Kilboun, A. Spielman and A.A. Lal, “Wild Primate population in Emerging Infectious Disease Research”, Emerging Infectious Diseases, vol. 4, no. 2, pp. 149 – 158, 1998. https://doi.org/10.3201/eid0402.98020.
[6] D. Sullivan, and The John Hopkins, “Malariology Overview: History, Lifecycle, Epidemiology, Pathology and Control”, School of Public Health, 2006.
[7] D.N.Breslauer; R.N. Maamari, N.A.Switz , W.A. Lam and D.A.Fletcher, “Mobile Phone Based Clinical Microscopy
for Global Health Applications’’ PLoS ONE 4: e6320, 2009. https://doi.org/10.1371/journal.pone.0006320
[8] C.W.Pirnstill and Coté GL, “Malaria Diagnosis Using a Mobile Phone Polarized Microscope”, Science Reports, 2015. https://doi.org/10.1038/srep13368
[9] L. Rosado, J. M. Correia da Costa, D. Elias. J. S. Cardoso, ‘‘Automated Detection of Malaria Parasites on Thick Blood Smears via Mobile Devices,’’ Procedia Computer Science, 2016. http://doi.org/10.1016/j.procs.2016.07.024
[10] J.A,Quinn, A. Andama, I Munabi and F.N, Kiwanuka, “Automated Blood Smear Analysis for Mobile Malaria Diagnosis,” Mobile Point Care Monitors Diagno Device Design. 2014.
[11] C. Dallet, S. Kareem and I. Kale, "Real time blood image processing application for malaria diagnosis using mobile phones," IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2405-2408, 2014. https://doi.org/ 10.1109/ISCAS.2014.6865657.
[12] F.Tek,A. Dempster, I. Kale, ‘‘Parasite Detection and Identification for Automated Thin Blood Film Malaria Diagnosis,’’ Computer vision and image understanding, vol. 114, no. 1, pp. 21–32, 2010. https://doi.org/10.1016/j.cviu.2009.08.003
[13] M.Maity, A. Maity, P. Dutta, C. Chakraborty, ‘‘A Web-Accessible Framework for Automated Storage with Compression and Textural Classification of Malaria Parasite Images,’’ International Journal of Computer Applications vol. 52, pp. 31–39, 2012. http://doi.org/10.5120/8279-1906
[14] A.Vedaldi, V. Gulshan, M. Varma, A. Zisserman, “Multiple Kernels for Object Detection”, IEEE 12th International Conference on Computer Vision (ICCV), pp. 606–613, 2009. http://doi.org/10.1109/ICCV.2009.5459183
[15] L. H. Miller, H.C. Ackerman, X.Z. Su,T.E, ‘‘Wellems Malaria Biology and Disease Pathogenesis: Insights for New Treatments,’’ Nature Medicine, vol. 19, pp. 156–167, 2013. http://doi.org/10.1038/nm.3073
[16] N. White, “The parasite Clearance Curve,” Malaria Journal, vol. 10, pp. 1–8, 2011. http://doi.org/10.1186/1475-2875-10-278
[17] L. S. Garcia, “Standards Ncfcl Laboratory Diagnosis of Blood-Borne Parasitic Diseases: Approved Guideline,” National Committee for Clinical Laboratory Standards, vol. 20, no:12, 2000.
[18] R.Turkki, M. Walliander. O. Ojansivu. N. Linder, M. Lundin, “An Open-Source MATLAB based Annotation Tool for Virtual Slides,” Diagnostic Pathology (Suppl 1) S30. 2013. https://doi.org/10.1186/1746-1596-8-S1-S30
[19] M. Walliander. R.Turkki, N. Linder, M. Lundin, J. Konsti, “Automated Segmentation of Blood Cells in Giemsa-Stained Digitized Thin Blood Films,” Diagnostic Pathology, vol. 8, 2013. http://doi.org/10.1186/1746-1596-8-S1-S37
[20] M, M. Cesario, S. Lundon. M. Luz. Masoodian and B. Rogers, “Mobile Support for Diagnosis of Communicable in Remote Locations,” ACM, 2012. https://doi.org/10.1145/2379256.2379261
[21] S. Herrera, A.F. Vallejo, J. P. Quintero. M. Arévalo-Herrera, Cancino and M, Ferro S, “Field Evaluation of an Automated RDT Reader and Data Management Device for Plasmodium Falciparum/Plasmodium Vivax Malaria in Endemic Areas of Colombia,” Malaria Journal, vol. 13, pp. 87. 2014. https://doi.org/10.1186/1475-2875-13-87
[22] O. Mudanyali, S. Dimitrov, U. Sikora, S. Padmanabhan I. Navruz and A, Ozcan, “Integrated Rapid Diagnostic-Test Reader Platform on a Cellphone”, Proceedings of The Second ACM Workshop on Mobile Systems, Applications, and Services For Healthcare, 2012. http://doi.org/10.1145/2396276.2396278
[23] D. M. S. Bibin, Nair and P. Punitha, “Malaria Parasite Detection from Peripheral Blood Smear Images Using Deep Belief Networks. IEEE Access, vol.5, pp. 9099- 9108, 2017. http://doi.org/10.1109/ACCESS.2017.2705642
[24] S. Rajaraman, K. Sameer. Antani, Mahdieh Poostchi, Kamolrat Silamut, Md. A. Hossain, Richard J. Maude, Stefan and George R. Thomas, “Pre-Trained Convolutional Neural as Feature Extractors Toward Improved Malaria Parasite Detection in Thin Blood Smear Images,” PeerJ Life and Environment,2018. http://.doi.org/10.7717/peerj.4568
[25] T, Sodeman, and G. Shute, “Identification of Malaria Parasites by Fluorescence Microscopy and Acridine Orange Staining” Bull World Health Organization, vol. 48, no. 5, pp. 591-596, 2024. https://iris.who.int/handle/10665/263852
[26] K. Suzuki, “Overview of Deep Learning in Medical Imaging,” Radiological Physics and Technology, 2017. https://doi.org/10.1007/s12194-017-0406-5
[27] Noppadon, Tangpukdee, C. Duangdee, P. Wilairatana and S. Krudsood, “Malaria Diagnosis, a Brief Review”, The Korean Journal of Parasitology, vol. 47, no. 2, pp. 93-102, 2009. https://doi.org/10.3347/kjp.2009.47.2.93
[28] Scott, “Using Customvision Ai and Building an Android Application Offline Image,” 2018.
[29] Microsoft Azure Custom Vision documentation, 2019: Microsoft Azure Cognitive Services, Cognitive services android sample,2019. https://github.com/Azure-Samples/cognitive-services-android-customvision- sample
[30] Jan, Z., Khan, A., Sajjad, M. et al. A review on automated diagnosis of malaria parasite in microscopic blood smears images”, Multimedia Tools and Applications, vol. 77, pp. 9801–9826, 2018. https://doi.org/10.1007/s11042-017-4495-2
[31] S. Jaeger, “National Library of Medicine Lister Hill National Center for Biomedical Communications Malaria Dataset” 2019.
[32] Z. Liang, A. Powell T., Ersoy, M. Poostchi, K. Silamut, K. Palaniappan, P. Guo. M, A. Hossain, A. Sameer., R. J Maude, J.X. Huang, S. Jaeger and Thomas, “CNN-Based Image Analysis for Malaria Diagnosis”, IEEE International Conference on Bioinformatics and Biomedicine, 2016. http://doi.org/10.1109/BIBM.2016.7822567
[33] D, K. Das, M. Ghosh. M. Pal, A.K. Maiti and C. Chakraborty, “Machine Learning Approach for Automated Screening of Malaria Parasites using Light Microscopic Images”, Micron, vol. 45, pp. 97-106, 2013. http://doi.org/10.1016/j.micron.2012.11.002
[34] D. Das, R. Mukherjee and C. Chakraborty, “Computational Microscopic Imaging for Malaria Parasite Detection: A Systematic Review,” Journal Microscopy. vol. 260, pp. 1–19, 2015. http://doi.org/10.1111/jmi.12270
[35] S. S. Devi, S. A. Sheikh and R. H. Laskar, “Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review. Int J Interact Multimedia Artificial,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, pp. 34–39, 2016. http://doi.org/10.9781/ijimai.2016.426
[36] Y. Dong, Z. Jiang, H. Shen, W. David Pan, L. A. Williams, V.V.B. Reddy, W. H. Benjamin and A. W. Bryan, “Evaluations of Deep Convolutional Neural Networks for Automatic Identification of Malaria-Infected Cells,” IEEE EMBS International Conference on Biomedical and Health Informatics,” pp. 101-104, 2017. http://doi.org/10.1109/BHI.2017.7897215
[37] G.P. Gopakumar, M. Swetha, G. Sai Siva and G.R. Sai Subrahmanyam, “Convolutional Neural Network- Based Malaria Diagnosis from a Focus Stack of Blood Smear Images Acquired Using a Custom-Built Slide Scanner,” Journal of Biophotonics, 2018. https://doi.org/10.1002/jbio.201700003
[38] K. He, X. Zhang. S. Ren and J, “Sun Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. https://doi.org/10.48550/arXiv.1512.03385
[39] P. Pereverzev, “An approach to Complex Model ECGA for the Stable and Unstable Grinding Conditions”, International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE), vol 971, 2020. https://doi.org/10.1088/1757-899X/971/2/022037
[40] P.Pereverzev, “Model of Processing Accuracy Prediction with Consideration of Multi-stage Process of Circular Grinding with Axial Feed,” IOP Conference Series: Materials Science and Engineering, vol. 709, 2020 https://doi.org/10.1088/1757-899X/709/3/033006
[41] E. E. Atojunere and G.E. Amiegbe, “Automated Leak and Water Quality Detection System for Piped Water Supply”, Malaysian Journal of Science, vol. 43, no. 3, pp. 98-108, 2024, https://doi.org/10.22452/mjs.vol43no3.11
[42] E.E.Atojunere, “Treatment Methods for Bitumen Polluted Water – A Review”, Malaysia Journal of Civil Engineering, University of Technology,Malaysia, vol. 36, no. 3, pp. 1-7, 2024 . https://doi.org/10.11113/mjce.v36.22022
[43] E. E. Atojunere and B. Omotoro, “Internet of Thıngs (IoT) Enabled Drip Irrigation System (DIS) for the growth of Allium Fistulosum Selcuk University Journal of Engineering Sciences, vol. 23, no. 3, 2024.
[44] E.E.Atojunere, and K. Ogedengbe, “Evaluating Water Quality Indicators of Some Water Sources in the Bitumen-Rich Areas of Ondo State, Nigeria”, International Journal of Environmental Pollution and Remediation (IJPER), vol. 7, no. 1, pp. 9-22, 2019. https:/doi.org/10.11159/ijepr.2019.002
[45] E.E. Atojunere, “Incidences of Bitumen Contamination of Water Sources in some Communities of Ondo State, Nigeria”, Malaysia Journal of Civil Engineering, University of Technology, Malaysia, vol. 33, no. 1, pp. 27–33, 2021. https://doi.org/10.11113/mjce.v33.16402.
[46] E. E. Atojunere, F.I. Oyeleke and S.O, “Afolayan. Significance of Soil Rheological Properties to Tillage in Ilorin North Central,” Nigeria Global Journal of Engineering & Technology, Institute Of Science And Technology, India, vol. 3, no. 2, pp. 250 – 254, 2010.
[47] Richard Budynas Shigley's, Keith Nisbett Mechanical Engineering Design. The International Student edition (ISE)McGraw-Hill Higher Education, 2024
[48] B.J.Bain, “Diagnosis from the Blood Smear,” The New-England Medical Review and Journal, vol. 353, pp. 498–507, 2005. http://doi.org/10.1056/NEJMra043442
[49] T. Ahonen, A. Hadid, M. Pietikäinen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 2037–2041, 2006. http://doi.org/10.1109/TPAMI.2006.244
[50] N. Linder. J. Konsti. R. Turkki. E. Rahtu, M. Lundin M., “Identification of Tumor Epithelium and Stroma in Tissue Microarrays Using Texture Analysis,” Diagnostic Pathology, vol. 7, no. 22, 2012 http://doi.org/10.1186/1746-1596-7-22
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Eganoosi Atojunere, Temilola. Adewunmi Onaneye
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract views: 89
,
PDF Downloads: 58
