ArticleComputational intelligence and neuroscience2022
Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- EfficientNet-B0-Based Screening of WBC-Diff Scattergram Images for Hematological Abnormalities.Cancer medicine · 2026Article
- Deep Learning-Based Malaria Parasite Image Classification on Real Microscopy Data.Infectious disease reports · 2026Article
- CT-Malaria Detection via Adaptive-Weighted Deep Learning Models.Biomedicines · 2026Article
- The Effect of Venipuncture Site on Hematology of Bats: Implications for Comparative Analyses.Integrative and comparative biology · 2025Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Article
- Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.Scientific reports · 2024Article
- Leveraging compact convolutional transformers for enhanced COVID-19 detection in chest X-rays: a grad-CAM visualization approach.Frontiers in big data · 2024Article
- Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials.PLoS pathogens · 2023Article
- An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5.Diagnostics (Basel, Switzerland) · 2023Article
- An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification.Frontiers in public health · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.
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