ArticleSensors (Basel, Switzerland)2023
Malaria Detection Using Advanced Deep Learning Architecture.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 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
- FedMal-XAI: an explainable federated vision transformer leveraging knowledge distillation for privacy-preserving malaria detection.Frontiers in public health · 2026Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- Leveraging MobileNetV2 and deep learning innovation for high accuracy Plasmodium Vivax detection in blood smears.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Article
- MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.Malaria journal · 2025Article
- Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.Scientific reports · 2025Article
- Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.International journal of environmental research and public health · 2024Article
- A transfer learning approach to identify Plasmodium in microscopic images.PLoS computational biology · 2024Article
- Efficient deep learning-based approach for malaria detection using red blood cell smears.Scientific reports · 2024Article
- An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.Parasites & vectors · 2024Article
- Supporting Malaria Diagnosis Using Deep Learning and Data Augmentation.Diagnostics (Basel, Switzerland) · 2024Article
- The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas.International journal of molecular sciences · 2024Review
- Impact of artificial intelligence and digital technology-based diagnostic tools for communicable and non-communicable diseases in Africa.African journal of laboratory medicine · 2024Review
Corrections and comments
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4 authors.
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Abstract
Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected mosquitoes. The early diagnosis and treatment of malaria are crucial for reducing morbidity and mortality rates, particularly in developing countries where the disease is prevalent. In this article, we present a novel convolutional neural network (CNN) architecture for detecting malaria from blood samples with a 99.68% accuracy. Our method outperforms the existing approaches in terms of both accuracy and speed, making it a promising tool for malaria diagnosis in resource-limited settings. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Additionally, we present an analysis of model performance on different subtypes of malaria and discuss the implications of our findings for the use of deep learning in infectious disease diagnosis.
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Registered trials
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