ArticleScientific reports2023
Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 51 citations in OpenAlex.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Image Processing and Deep Convolutional Neural Network Method for Automated Malaria Parasite Detection in Thin Blood Slide Images.Diagnostics (Basel, Switzerland) · 2026Article
- DeepStackVEGF a stacking ensemble deep learning framework for vascular endothelial growth factor prediction.Scientific reports · 2026Article
- Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis.Scientific reports · 2026Article
- Intelligent automation cognitive system for accurate malaria diagnosis using digital blood smears.PloS one · 2026Article
- Deep learning-assisted malaria microscopy with sensitivity-aware threshold optimization.Frontiers in medicine · 2026Article
- Mobile phone-based plasmodium parasites stage detection from Giemsa stained blood smear by convolutional neural networks.Parasitology research · 2025Article
- Article
- 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
- Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images.Frontiers in cellular and infection microbiology · 2025Article
- Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic.BMC infectious diseases · 2024Article
- Hybrid framework for membrane protein type prediction based on the PSSM.Scientific reports · 2024Article
- The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas.International journal of molecular sciences · 2024Review
- Embedded-deep-learning-based sample-to-answer device for on-site malaria diagnosis.Frontiers in bioengineering and biotechnology · 2024Article
- Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis.Journal of clinical medicine · 2023Review
- From data to decisions: The vital role of digital epidemiology for strengthening public health.Digital healthReview
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Authors and funding
5 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is an acute fever sickness caused by the Plasmodium parasite and spread by infected Anopheles female mosquitoes. It causes catastrophic illness if left untreated for an extended period, and delaying exact treatment might result in the development of further complications. The most prevalent method now available for detecting malaria is the microscope. Under a microscope, blood smears are typically examined for malaria diagnosis. Despite its advantages, this method is time-consuming, subjective, and requires highly skilled personnel. Therefore, an automated malaria diagnosis system is imperative for ensuring accurate and efficient treatment. This research develops an innovative approach utilizing an urgent, inception-based capsule network to distinguish parasitized and uninfected cells from microscopic images. This diagnostic model incorporates neural networks based on Inception and Imperative Capsule networks. The inception block extracts rich characteristics from images of malaria cells using a pre-trained model, such as Inception V3, which facilitates efficient representation learning. Subsequently, the dynamic imperative capsule neural network detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The experiment results demonstrate a significant improvement in malaria parasite recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient diagnostic solutions by leveraging state-of-the-art technologies to combat malaria.
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