Deep Learning-Based Detection of Malaria Infection Through Blood Sample Analysis for Malaria Diagnosis
Keywords:
Malaria disease, Deep learning, Image processing, Disease classification, Internet of Medical Things.Abstract
Malaria, a life-threatening disease caused by Plasmodium parasites transmitted through infected mosquitoes, remains a significant public health concern in many regions worldwide. Early and accurate detection of malaria infection is crucial for timely treatment and disease management. The automated malaria detection system can be integrated into portable diagnostic devices, enabling healthcare professionals to perform rapid and accurate malaria tests in remote or resource-limited settings. The system can assist researchers and health organizations in tracking malaria prevalence and monitoring its spread, contributing to epidemiological studies and efficient resource allocation. Conventional methods for malaria detection involve manual examination of blood smears under a microscope by trained technicians. Although reliable, this process is time-consuming, labour-intensive, and dependent on the expertise of the microscopist. The regression-based examination of blood smears introduces the potential for errors, leading to false-negative or false-positive results. In recent years, deep learningbased approaches have shown promising results in automating the detection of malaria parasites through blood sample analysis. This work presents an advanced machine learning-based method for the automated detection of malaria infection, leveraging image processing techniques to achieve high accuracy and efficiency.
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References
. WHO. World Malaria Report 2022. Available online: https://www.who.int/teams/globalmalaria-programme/reports/world-malaria-report-2022 (accessed on 1 March 2024).
. WHO. World Malaria Report 2021: An In-Depth Update on Global and Regional Malaria Data
and Trends. Available online: https://www.who.int/teams/global-malariaprogramme/reports/world-malaria-report-2021 (accessed on 1 May 2024).
. Yang, F.; Poostchi, M.; Yu, H.; Zhou, Z.; Silamut, K.; Yu, J.; Maude, R.J.; Jaeger, S.; Antani, S.
Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J.
Biomed. Health Inform. 2019, 24, 1427–1438.
. World Health Organization. Malaria Microscopy Quality Assurance Manual, 2nd ed.; World
Health Organization: Geneva, Switzerland, 2016; Available online:
https://www.who.int/docs/default-source/documents/publications/gmp/malaria-microscopyquality-assurance-manual.pdf (accessed on 2 March 2024).
. Rawat, W.; Wang, Z. Deep convolutional neural networks for image classification: A
comprehensive review. Neural Comput. 2017, 29, 2352–2449.
. Colubri, A.; Silver, T.; Fradet, T.; Retzepi, K.; Fry, B.; Sabeti, P. Transforming clinical data into
actionable prognosis models: Machine-learning framework and field-deployable app to predict
outcome of Ebola patients. PLoS Negl. Trop. Dis. 2016, 10, e0004549. [Green Version]
. Smith, K.P.; Kirby, J.E. Image analysis and artificial intelligence in infectious disease
diagnostics. Clin. Microbiol. Infect. 2020, 26, 1318–1323.
. Das, D.K.; Ghosh, M.; Pal, M.; Maiti, A.K.; Chakraborty, C. Machine learning approach for
automated screening of malaria parasite using light microscopic images. Micron 2013, 45, 97–
. Bibin, D.; Nair, M.S.; Punitha, P. Malaria parasite detection from peripheral blood smear images
using deep belief networks. IEEE Access 2017, 5, 9099–9108.
. Gopakumar, G.P.; Swetha, M.; Sai Siva, G.; Sai Subrahmanyam, G.R.K. Convolutional neural
network-based malaria diagnosis from focus stack of blood smear images acquired using custombuilt slide scanner. J. Biophotonics 2018, 11, e201700003.
. Dandekar, R.; Rackauckas, C.; Barbastathis, G. A machine learning-aided global diagnostic
and comparative tool to assess effect of quarantine control in COVID-19 spread. Patterns 2020,
, 100145.
. Baldominos, A.; Puello, A.; Oğul, H.; Aşuroğlu, T.; Colomo-Palacios, R. Predicting infections
using computational intelligence–a systematic review. IEEE Access 2020, 8, 31083–31102.
. O’Shea, K.; Nash, R. An introduction to convolutional neural networks. arXiv 2015,
arXiv:1511.08458.
. Sadeghi-Tehran, P.; Angelov, P.; Virlet, N.; Hawkesford, M.J. Scalable database indexing and
fast image retrieval based on deep learning and hierarchically nested structure applied to remote
sensing and plant biology. J. Imaging 2019, 5, 33.
. Wu, J. Introduction to Convolutional Neural Networks; National Key Lab for Novel Software
Technology, Nanjing University: Nanjing, China, 2017; Volume 5, p. 495.
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