ArticleIEEE journal of biomedical and health informatics2021
Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears.
Article in IEEE journal of biomedical and health informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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30 citing papers in PubMed, 104 citations in OpenAlex.
- Deep learning applications in parasite microscopy: A scoping review.PLoS neglected tropical diseases · 2026Article
- Capturing fine-grained spatial details in brain tumor MRI via multi-module feature enhancement.Frontiers in oncology · 2026Article
- 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
- 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
- Automated multi-model framework for malaria detection using deep learning and feature fusion.Scientific reports · 2025Article
- Computer Viewing Model for Classification of Erythrocytes Infected withMedicina (Kaunas, Lithuania) · 2025Article
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- Fusion of Visible and Infrared Aerial Images from Uncalibrated Sensors Using Wavelet Decomposition and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review.Molecules (Basel, Switzerland) · 2024Review
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- CMTT-JTracker: a fully test-time adaptive framework serving automated cell lineage construction.Briefings in bioinformatics · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- 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
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine.BMC infectious diseases · 2024Article
- Exploring the Impact of Batch Size on Deep Learning Artificial Intelligence Models for Malaria Detection.Cureus · 2024Article
- Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.Transboundary and emerging diseases · 2024Article
- Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features.Knowledge-based systems · 2023Article
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97 %. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.