ArticleSensors (Basel, Switzerland)2022
Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- MalariaNet: A Microcontroller-Deployable Malaria-Microscopy Detector for Point-of-Care Biosensing Under Leakage-Free Evaluation.Biosensors · 2026Article
- ISTNet: a multi-scale transformer-based architecture for malaria cell classification.Medical & biological engineering & computing · 2026Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.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
- Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.BMC medical informatics and decision making · 2025Article
- Improving Malaria diagnosis through interpretable customized CNNs architectures.Scientific reports · 2025Article
- Real-time jute leaf disease classification using an explainable lightweight CNN via a supervised and semi-supervised self-training approach.Frontiers in plant science · 2025Article
- Stain-free artificial intelligence-assisted light microscopy for the identification of blood cells in microfluidic flow.Frontiers in bioinformatics · 2025Article
- Article
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
- Advances in Malaria Diagnostic Methods in Resource-Limited Settings: A Systematic Review.Tropical medicine and infectious disease · 2024Review
- Biophysical profiling of red blood cells from thin-film blood smears using deep learning.Heliyon · 2024Article
- Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization.PeerJ. Computer science · 2024Article
- Multiclass malaria parasite recognition based on transformer models and a generative adversarial network.Scientific reports · 2023Article
- Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa.Parasites & vectors · 2023Article
- Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images.Journal of imaging · 2023Article
- ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network.Expert systems with applications · 2023Article
- Article
- Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review.Frontiers in microbiology · 2022Review
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7 authors.
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Abstract
Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.
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