PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites
Version 1
: Received: 12 February 2024 / Approved: 21 February 2024 / Online: 21 February 2024 (10:19:00 CET)
Version 2
: Received: 19 March 2024 / Approved: 19 March 2024 / Online: 20 March 2024 (13:13:29 CET)
How to cite:
El-Sunais, Y.; Eberemu, N.C. Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites. Preprints2024, 2024021217. https://doi.org/10.20944/preprints202402.1217.v1
El-Sunais, Y.; Eberemu, N.C. Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites. Preprints 2024, 2024021217. https://doi.org/10.20944/preprints202402.1217.v1
El-Sunais, Y.; Eberemu, N.C. Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites. Preprints2024, 2024021217. https://doi.org/10.20944/preprints202402.1217.v1
APA Style
El-Sunais, Y., & Eberemu, N.C. (2024). Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites. Preprints. https://doi.org/10.20944/preprints202402.1217.v1
Chicago/Turabian Style
El-Sunais, Y. and Nkiru Charity Eberemu. 2024 "Revolutionizing Parasitic Infection Diagnosis in Northern Nigeria: An AI-Based Approach for Accurate Identification and Counting of Intestinal Parasites" Preprints. https://doi.org/10.20944/preprints202402.1217.v1
Abstract
Intestinal parasitic infections pose a significant public health challenge in Northern Nigeria, with prevalence rates ranging from 20% to 70%. Traditional diagnostic methods, primarily microscopic examination of stool samples, face limitations such as low sensitivity and high costs. This research addresses these challenges by proposing an Artificial Intelligence (AI)-based platform for the identification and counting of intestinal parasites. Leveraging the You Only Look Once (YOLO) V8 model, trained on a dataset of 360 pre-processed and annotated images, the AI model demonstrated promising performance metrics. The precision-recall curve, average precision, mean average precision, and F1 score indicated reliable detection and classification across various parasite classes. The model exhibited a well-balanced trade-off between precision and recall, showcasing its potential as a cost-effective and accessible tool for improving the diagnosis and treatment of intestinal parasitic infections in resource-limited settings.
Keywords
Intestinal parasitic infections, Artificial Intelligence (AI), Public health, Diagnostic methods, Parasite identification, You Only Look Once (YOLO) V8
Subject
Biology and Life Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
