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., Izuafa Braimah A. · International Journal of Research and Innovation in Applied Science · 2024
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.