ArticleDiagnostics (Basel, Switzerland)2020
Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and its Smartphone Based Application.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- PathoSafe: Sub-1 KB Out-of-Distribution Rejection and Cluster-Aware Certification for Point-of-Care Pathogen Biosensing.Biosensors · 2026Article
- MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing Under Leakage-Free Evaluation.Biosensors · 2026Article
- Automated Malaria Ring Form Classification in Blood Smear Images Using Ensemble Parallel Neural Networks.Journal of imaging · 2026Article
- Application of artificial intelligence in geriatric infection: recent advances and prospects.Frontiers in cellular and infection microbiology · 2026Review
- Trustworthy deep learning for malaria diagnosis using explainable artificial intelligence.Scientific reports · 2025Review
- Improving Malaria diagnosis through interpretable customized CNNs architectures.Scientific reports · 2025Article
- Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.Scientific reports · 2025Article
- Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing.Parasites & vectors · 2025Article
- Article
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
- Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging.Computational and structural biotechnology journal · 2024Article
- Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.Sensors (Basel, Switzerland) · 2024Review
- Development of intelligent suite for malaria pathogen detection in microscopy images.Scientific reports · 2024Article
- Design of a novel intelligent computing framework for predictive solutions of malaria propagation model.PloS one · 2024Article
- Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment.Tropical diseases, travel medicine and vaccines · 2023Article
- Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.Scientific reports · 2022Article
- Reducing data dimension boosts neural network-based stage-specific malaria detection.Scientific reports · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduced with the development of an automatic accurate and efficient model. In this article, we propose an entirely automated Convolutional Neural Network (CNN) based model for the diagnosis of malaria from the microscopic blood smear images. A variety of techniques including knowledge distillation, data augmentation, Autoencoder, feature extraction by a CNN model and classified by Support Vector Machine (SVM) or K-Nearest Neighbors (KNN) are performed under three training procedures named
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