ArticleJournal of imaging2022
An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.Biomedicines · 2026Article
- ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs.Journal of imaging · 2025Article
- Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images.Frontiers in cellular and infection microbiology · 2025Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.Parasites & vectors · 2024Article
- Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system.PloS one · 2024Article
- YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection.Journal of imaging · 2023Article
- Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks.Scientific reports · 2023Article
- Article
- An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification.Frontiers in public health · 2022Article
- A novel deep learning-assisted hybrid network for plasmodium falciparum parasite mitochondrial proteins classification.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a globally widespread disease caused by parasitic protozoa transmitted to humans by infected female mosquitoes of Anopheles. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analysing digital microscopic blood smears, which is tedious, time-consuming and error prone. So, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work focuses on deep learning-based models, by comparing off-the-shelf architectures for classifying healthy and parasite-affected cells, by investigating the four-class classification on the Plasmodium falciparum stages of life and, finally, by evaluating the robustness of the models with cross-dataset experiments on two different datasets. The main contributions to the research in this field can be resumed as follows: (i) comparing off-the-shelf architectures in the task of classifying healthy and parasite-affected cells, (ii) investigating the four-class classification on the
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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.