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
- Sokoto State University, Sokoto, Sokoto State 42
- St. Albert The Great Major Seminary, Abeokuta. (affl. To University of Benin) 1
- Sule Lamido University, Kafin Hausa, Jigawa State 4
- Tai Solarin University of Education, Ijagun, Ogun State 18
- Tansian University, Oba, Anambra State 1
- Taraba State University, Jalingo, Taraba State 32
- Temple-Gate Polytechnic, Osisioma, Abia State 1
- The Oke-Ogun Polytechnic, Saki, Oyo State 6
- The Polytechnic, Ibadan, Oyo State 13
- THOMAS ADEWUMI UNIVERSITY, OKO-IRESE, KWARA STATE 1