Advancing Malaria Detection: A Comparative Study and Proposal for Web-Based Predictive Application Utilizing Convolutional Neural Network and TensorFlow

Egwu Samuel Onuche-Ojo, Eseyin Joseph B, Dako Apaleokhai D and Izuafa Braimah A.
Additional contact information
Egwu Samuel Onuche-Ojo: Dept of Software Engineering, Veritas University Abuja, Abuja, Nigeria
Eseyin Joseph B: ICT Directorate, University of Jos, Jos Nigeria
Dako Apaleokhai D: Dept of Software Engineering, Veritas University Abuja, Nigeria
Izuafa Braimah A.: Dept of Software Engineering, Veritas University Abuja, Nigeria

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 6, 222-232

Abstract: Malaria is a major public health problem in developing countries. The prevalence of malaria is increasing year by year, resulting in a decrease in the number of deaths and morbidity. Malaria has become a serious public health issue worldwide, particularly in low resource underserved rural communities. There is an urgent need for a web-based predictive application using TensorFlow and Convolutional Neural Networks (CNNs) for malaria detection. This paper provides a comparative overview of how the malaria situation has changed over time, showing which countries have maintained indigenous cases and which have progressed to different statuses by 2022. The goal of the paper is to showcase the efficacy of machine learning, particularly CNN and TensorFlow models, in detecting malaria using cell images. Moreover, the integration of a Web-Based Predictive System further enhances the accessibility and efficiency of our diagnostic tools, potentially contributing to better healthcare outcomes, especially in malaria-endemic regions.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrias/ ... -issue-6/222-232.pdf (application/pdf)
https://rsisinternational.org/journals/ijrias/arti ... work-and-tensorflow/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:9:y:2024:i:6:p:222-232

Access Statistics for this article

International Journal of Research and Innovation in Applied Science is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Innovation in Applied Science from International Journal of Research and Innovation in Applied Science (IJRIAS)
Bibliographic data for series maintained by Dr. Renu Malsaria ().