ArticleScientific reports2025
Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevalence of malaria and its major parasite species in Bangladesh: a systematic review and meta-analysis.Malaria journal · 2026Pooled it
- Artificial intelligence enabled performance evaluation of an enhanced SPR biosensor for malaria diagnosis.Scientific reports · 2026Article
- A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.Parasites & vectors · 2026Article
- Deep learning-assisted malaria microscopy with sensitivity-aware threshold optimization.Frontiers in medicine · 2026Article
- Artificial intelligence for precision malaria control: transforming surveillance, prediction, and intervention strategies.Frontiers in digital health · 2026Review
- Dermoscopically informed deep learning model for classification of actinic keratosis and cutaneous squamous cell carcinoma.Scientific reports · 2025Article
- Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks.Parasitology research · 2025Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- Enhancing Physicians' Adherence to the 2023 Sudan Malaria Case Management Protocol Using AI as an Intervention Tool.Cureus · 2025Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI) advances, particularly convolutional neural networks (CNNs), have significantly improved diagnostic capabilities and accuracy by enabling the automated analysis of medical images. Previous models efficiently detected malaria parasites in red blood cells but had difficulty differentiating between species. We propose a CNN-based model for classifying cells infected by P. falciparum, P. vivax, and uninfected white blood cells from thick blood smears. Our best-performing model utilizes a seven-channel input and correctly predicted 12,876 out of 12,954 cases. We also generated a cross-validation confusion matrix that showed the results of five iterations, achieving 63,654 out of 64,126 true predictions. The model's accuracy reached 99.51%, a precision of 99.26%, a recall of 99.26%, a specificity of 99.63%, an F1 score of 99.26%, and a loss of 2.3%. We are now developing a system based on real-world quality images to create a comprehensive detection tool for remote regions where trained microscopists are unavailable.
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