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Updated: Jul 5, 2026
Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs
Published on: April 21, 2012
A new diagnostic method and tool for cutaneous leishmaniasis based on artificial intelligence techniques
Said Gadri1, Soufyane Bounab1, Nabil Benazi2
1Laboratory of Informatics and its Applications of M'sila LIAM, Department of Computer Science, Faculty of Mathematics and Informatics, Univ. Mohamed Boudiaf of M'sila, Road Bordj Bou Arreridj, CP 28000, Algeria.
Background:
Cutaneous leishmaniasis (CL) is a parasitic disease caused by protozoan parasites of the genus Leishmania, leading to significant morbidity in endemic regions. While effective, traditional diagnostic methods often suffer from limitations such as the requirement for specialized expertise and prolonged processing times. Artificial intelligence (AI) methodologies have recently emerged to enhance CL's diagnostic accuracy and efficiency.
Objective:
This project aims to develop and make available to biologists a new, rapid, more efficient, and more precise cutaneous leishmaniasis diagnosis method and tool based on the latest techniques of artificial intelligence AI and computer vision (CV).
Methods:
We used a deep learning model (YOLO 8) to detect Leishmania parasite bodies in microscopic images; we trained the model on microscopic images collected at the Algerian Pasteur Institute, Annex of M'sila. We implemented the proposed model on a mobile application to validate its performance.
Results:
YOLO v8's application to the detection of Leishmania parasite bodies in microscopic images gives a high accuracy of 97 % over the entire test dataset.
Conclusion:
This research demonstrated the significant potential of AI-based object detection models, particularly YOLOv8, for accurately detecting Leishmania parasites in microscopic images. The obtained results pave the way for promising clinical applications and further research in this field.
