ArticleArtificial intelligence review2023
A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.
Article in Artificial intelligence review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 104 citations in OpenAlex.
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- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- A benchmark for environmental microorganism object detection on EMDS-7.Frontiers in microbiology · 2026Article
- WPF-Mamba: wavelet-based progressive multispectral fusion mamba for fine-grained microorganism detection.Frontiers in microbiology · 2026Article
- YOLO-TME: A UAV landing detection algorithm that is suitable for Polar ice Floe base stations.Scientific reports · 2025Article
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- Tumor microenvironment and immune-related myositis: addressing muscle wasting in cancer immunotherapy.Frontiers in immunology · 2025Review
- Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions.Frontiers in plant science · 2025Review
- Optimizing neurointerventional procedures: an algorithm for embolization coil detection and automated collimation to enable dose reduction.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.Sensors (Basel, Switzerland) · 2024Article
- Super resolution-based methodology for self-supervised segmentation of microscopy images.Frontiers in microbiology · 2024Article
- A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images.Scientific reports · 2023Article
- Parasitic egg recognition using convolution and attention network.Scientific reports · 2023Article
- EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation.Frontiers in microbiology · 2023Article
- An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network.Frontiers in bioengineering and biotechnology · 2023Article
- A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples.Frontiers in microbiology · 2023Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.
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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.