ArticleBMC infectious diseases2022
A machine learning-based system for detecting leishmaniasis in microscopic images.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 50 citations in OpenAlex.
- Testing the feasibility of deep learning approaches to enhance monitoring of marine macroinvertebrates: Insights from a case study using the gastropod Peringia ulvae.Environmental monitoring and assessment · 2026Article
- Feasibility study of an AI-powered mobile app to support cutaneous leishmaniasis diagnosis in the Brazilian Amazon.PLoS neglected tropical diseases · 2026Article
- Decision-support system for live detection of Leishmania parasites from microscopic images with deep learning.BMC infectious diseases · 2026Article
- Unveiling the ecology and spatial dynamics of Trypanosoma cruzi, its DTUs and Triatoma vitticeps in the Atlantic Forest of south-eastern Espírito Santo State, Brazil.PLoS neglected tropical diseases · 2026Article
- AIR-LEISH: A Dataset of Giemsa-Stained Microscopy Images for AI-based Leishmania amastigotes Detection.Scientific data · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Pathogenic Significance of Trypanosomatids: Progress in Drug Resistance, Control Strategies, and Artificial Intelligence.Interdisciplinary perspectives on infectious diseases · 2026Review
- A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.PloS one · 2026Article
- Leishmaniasis in Morocco: Epidemiology, Transmission Dynamics, and the Potential of Artificial Intelligence for Disease Management.Journal of parasitology research · 2026Review
- Molecular diagnostics for cutaneous leishmaniasis: progress towards fulfilling the WHO target product profile.Parasitology · 2025Review
- A Non-Invasive Diagnostic Platform for Canine Leishmaniasis Using VOC Analysis and Distributed Veterinary Infrastructure.Veterinary sciences · 2025Article
- Advances and Challenges in the Diagnosis of Leishmaniasis.Molecular diagnosis & therapy · 2025Review
- Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions.Transboundary and emerging diseases · 2025Article
- Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review.Molecules (Basel, Switzerland) · 2024Review
- Molecular Diagnosis of Human Cutaneous Leishmaniasis and Identification of the Causative Leishmania Species in Iran: A Narrative ReviewIranian biomedical journal · 2024Review
- DeepLeish: a deep learning based support system for the detection of Leishmaniasis parasite from Giemsa-stained microscope images.BMC medical imaging · 2024Article
- A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine.BMC infectious diseases · 2024Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLeishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis.
methodsWe used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier.
resultsA 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages.
conclusionThe developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.