ArticleArtificial intelligence review2023
Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer.
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 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.Comprehensive reviews in food science and food safety · 2026Pooled it
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- A novel hybrid approach for microbial detection using deep ensemble learning.BMC microbiology · 2026Article
- High-Resolution Colony Images of Clinically Isolated Bacteria for Automated Detection and Deep Learning.Scientific data · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Multidimensional strategy enables scalable metabolome diversity in microbial fermentations.Scientific reports · 2026Article
- Accelerated detection ofMicrobiology spectrum · 2026Article
- EMDS-7-FSCIL: a benchmark for Few-Shot Class-Incremental Learning in environmental microorganism recognition.Frontiers in microbiology · 2026Article
- WPF-Mamba: wavelet-based progressive multispectral fusion mamba for fine-grained microorganism detection.Frontiers in microbiology · 2026Article
- TRACE: reconstructing fragmented microbial landscapes for high-resolution antimicrobial resistance surveillance.Frontiers in microbiology · 2026Article
- Hierarchical context enhancement for long-tail entity retrieval augmented generation.Frontiers in artificial intelligence · 2026Article
- Image Dataset of Microfauna in Activated Sludge.Scientific data · 2025Article
- MACNeXt-Based Bacteria Species Detection.Microorganisms · 2025Article
- Review of Hydrogen Sensors in Aerobic and Anaerobic Environments Coupled with Artificial Intelligence Tools.Sensors (Basel, Switzerland) · 2025Review
- ProtoMAP: prototypical network based few-shot learning for missed abortion prediction.BMC medical informatics and decision making · 2025Article
- Development and Validation of an Explainable Prediction Model to Assess the Risk of Coronary Artery Disease in Young and Middle-Aged Individuals.Reviews in cardiovascular medicine · 2025Article
- Computer vision based efficient segmentation and classification of multi brain tumor using computed tomography images.Scientific reports · 2025Article
- Deep Layered Network Based on Rotation Operation and Residual Transform for Building Segmentation from Remote Sensing Images.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are presented. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.
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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.