Design and Implementation of Malaria Disease Detection System Using Convolutional Neural Network
Student: Mujitaba ADAM KABIR (Project, 2025)
Department of Computer Science
Northwest University, Kano, Kano State
Abstract
Malaria remains a significant global health challenge, particularly in resource-limited regions where traditional diagnostic methods like microscopy and Rapid Diagnostic Tests (RDTs) are time-consuming, labor-intensive, and prone to errors. This project aims to address these limitations by developing an AI-driven malaria detection system using Convolutional Neural Networks (CNNs). The system analyzes blood smear images to classify them as malaria-positive or negative. A dataset of 27,558 images from Kaggle was used to train and evaluate the model. The system achieved high accuracy, precision, recall, and F1-score, demonstrating its potential to improve malaria diagnosis. Usability testing revealed a 93% task completion rate and a 4.5/5 user satisfaction score, indicating the system's effectiveness and user-friendliness. The project highlights the potential of deep learning in healthcare and provides a foundation for future work, including clinical validation and real-time deployment.
Keywords
For the full publication, please contact the author directly at: mujitabakabir6@gmail.com
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Institutions
- Covenant Polytechnic, Aba, Abia State 1
- Covenant University, Canaan Land, Ota, Ogun State 4
- Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State 2
- Crescent University, Abeokuta, Ogun State 1
- Cross Rivers University of Technology, Calabar, Cross Rivers State 142
- Delta State Polytechnic, Ogwashi-Uku, Delta State 11
- Delta State Polytechnic, Otefe, Delta State 12
- Delta State University, Abraka, Delta State 138
- Ebonyi State University, Abakaliki, Ebonyi State 17
- Edo University, Iyamho, Edo State 10