ArticleScientific reports2024
Efficient deep learning-based approach for malaria detection using red blood cell smears.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 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
- Deep Learning-Based Malaria Parasite Image Classification on Real Microscopy Data.Infectious disease reports · 2026Article
- MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing Under Leakage-Free Evaluation.Biosensors · 2026Article
- A hybrid machine learning approach for automated malaria diagnosis from thin blood smear images.Parasites & vectors · 2026Article
- Applied Nanobiotechnology for Vector-borne Disease Management: Drug Delivery, Diagnostics, and Sustainable Vector Control.Applied biochemistry and biotechnology · 2026Review
- Artificial intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy.PLoS neglected tropical diseases · 2026Article
- CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.Biomedicines · 2026Article
- Automated Malaria Ring Form Classification in Blood Smear Images Using Ensemble Parallel Neural Networks.Journal of imaging · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Blood Parasites and Wildlife: The Development of a Discipline.Integrative zoology · 2026Review
- Deep learning-assisted malaria microscopy with sensitivity-aware threshold optimization.Frontiers in medicine · 2026Article
- Advanced ANN-LMB modeling of hepatitis B transmission across sexual networks and its disability burden.Scientific reports · 2025Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- DANet a lightweight dilated attention network for malaria parasite detection.Scientific reports · 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
- 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
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images.Frontiers in cellular and infection microbiology · 2025Article
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7 authors.
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Abstract
Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.
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