ArticleSensors (Basel, Switzerland)2017
Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 52 citations in OpenAlex.
- An interactive mindmap of blood film images for automated malaria diagnosis: a comprehensive metadata repository of available datasets.Bioinformatics advances · 2026Review
- Scoping review of artificial intelligence via mobile technology and social media for health in Africa.Nature communications · 2025Article
- A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.Journal of imaging informatics in medicine · 2025Article
- Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.Sensors (Basel, Switzerland) · 2024Review
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
- An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5.Diagnostics (Basel, Switzerland) · 2023Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
- Innovations in3 Biotech · 2021Review
- Systematic Review and Meta-Analysis of the Diagnostic Accuracy of Mobile-Linked Point-of-Care Diagnostics in Sub-Saharan Africa.Diagnostics (Basel, Switzerland) · 2021Review
- Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance.PLoS neglected tropical diseases · 2021Article
- Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis.IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop · 2021Article
- Malaria Screener: a smartphone application for automated malaria screening.BMC infectious diseases · 2020Article
- Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers.Diagnostic pathology · 2020Article
- Using autoencoders as a weight initialization method on deep neural networks for disease detection.BMC medical informatics and decision making · 2020Article
- Detection of Intestinal Protozoa in Trichrome-Stained Stool Specimens by Use of a Deep Convolutional Neural Network.Journal of clinical microbiology · 2020Article
- Recent Advances of Malaria Parasites Detection Systems Based on Mathematical Morphology.Sensors (Basel, Switzerland) · 2018Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.
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Registered trials
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.