ReviewArchives of computational methods in engineering : state of the art reviews2022
Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments.
Review in Archives of computational methods in engineering : state of the art reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.International journal of clinical pharmacy · 2026Article
- Automated leukemia detection from microscopic images using deep transfer learning with explainable AI-based analysis.Scientific reports · 2026Article
- Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support.Frontiers in cellular and infection microbiology · 2026Article
- Microscopic image dataset of fungal spores for computer vision applications in Tectona grandis and other taxa.Scientific data · 2025Article
- Molecular Docking and Target-Specific Binding Profiles of Benzosuberane-Based Compounds.ChemMedChem · 2025Review
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Biocontrol Effect ofMicroorganisms · 2025Article
- Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.Scientific reports · 2024Article
- Quantifying entanglement for unknown quantum states via artificial neural networks.Scientific reports · 2024Article
- A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition.Biomimetics (Basel, Switzerland) · 2024Article
- Article
- UMicroorganisms · 2024Article
- Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence.Annals of general psychiatry · 2024Article
- Roadmap on computational methods in optical imaging and holography [invited].Applied physics. B, Lasers and optics · 2024Review
- Deep learning diagnostic performance and visual insights in differentiating benign and malignant thyroid nodules on ultrasound images.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Developments in Image Processing Using Deep Learning and Reinforcement Learning.Journal of imaging · 2023Review
- Yeast cell detection using fuzzy automatic contrast enhancement (FACE) and you only look once (YOLO).Scientific reports · 2023Article
- An Efficient and Effective Framework for Intestinal Parasite Egg Detection Using YOLOv5.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization.Biomimetics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.
Identifiers
What Socratic holds
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 Socratic graph.