Kernel Metadata Declaration — on demand

What the DOI identifies

Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis

10.46243/jst.2023.v8.i12.pp185-198 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-12-12

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 12 · pp. 185–198

Principal agents

• N. Teja N. Teja (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 17 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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    "abstract": {
        "value": "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, labor-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, machine learning-based 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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            "unstructured": "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-malaria-programme/reports/ world-malaria-report-2021 (accessed on 1 September 2022)"
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            "unstructured": "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"
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            "unstructured": "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-microscopy-quality-assurancemanual.pdf (accessed on 2 March 2023)"
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            "doi": "10.1371/journal.pntd.0004549",
            "unstructured": "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]"
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            "doi": "10.1016/j.cmi.2020.03.012",
            "unstructured": "Smith, K.P.; Kirby, J.E. Image analysis and artificial intelligence in infectious disease diagnostics. Clin. Microbiol. Infect. 2020, 26, 1318–1323"
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            "unstructured": "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–"
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            "unstructured": "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 custom-built slide scanner. J. Biophotonics 2018, 11, e201700003"
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            "unstructured": "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, 1, 100145"
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            "doi": "10.1109/access.2020.2973006",
            "unstructured": "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. N. Teja, K. Vyshnavi, M. Pavana Sri, K. Bharathi: Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis"
        },
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            "key": "ref13",
            "unstructured": "O’Shea, K.; Nash, R. An introduction to convolutional neural networks. arXiv 2015, arXiv:1511.08458"
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            "unstructured": "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. [Green Version]"
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            "key": "ref15",
            "unstructured": "Wu, J. Introduction to Convolutional Neural Networks; National Key Lab for Novel Software Technology, Nanjing University: Nanjing, China, 2017; Volume 5, p. 495. [Google Scholar]"
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            "key": "ref16",
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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