ArticleScientific data2023
Annotated dataset for deep-learning-based bacterial colony detection.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Advances in Foodborne Pathogen Detection: From Conventional Confirmation to Integrated and Intelligent Platforms.Foods (Basel, Switzerland) · 2026Review
- Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.Scientific reports · 2026Article
- Deep neural networks in medical microbiology for bacterial colonies classification.Scientific reports · 2026Article
- High-Resolution Colony Images of Clinically Isolated Bacteria for Automated Detection and Deep Learning.Scientific data · 2026Article
- An annotated dataset of Gram stains from positive blood cultures.Scientific data · 2026Article
- A weakly supervised framework for automated biological assay assessment.Virus research · 2026Article
- MACNeXt-Based Bacteria Species Detection.Microorganisms · 2025Article
- Two-Stage Hierarchical Pruning (THP-CNN) of Convolutional Neural Networks for Rapid Pathogenic Bacterial Detection Using High-Resolution Colony Images in Intensive Care Units.Diagnostics (Basel, Switzerland) · 2025Article
- Implementation of convolutional neural networks for microbial colony recognition.Microbiology spectrum · 2025Article
- Colony-YOLO: A Lightweight Micro-Colony Detection Network Based on Improved YOLOv8n.Microorganisms · 2025Article
- Improving the odds: Artificial intelligence and the great plate count anomaly.Microbial biotechnology · 2024Article
- A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation.Scientific data · 2024Article
- Bacterial colony size growth estimation by deep learning.BMC microbiology · 2023Article
- Annotated dataset for deep-learning-based bacterial colony detection.Scientific data · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Quantifying bacteria per unit mass or volume is a common task in various fields of microbiology (e.g., infectiology and food hygiene). Most bacteria can be grown on culture media. The unicellular bacteria reproduce by dividing into two cells, which increases the number of bacteria in the population. Methodologically, this can be followed by culture procedures, which mostly involve determining the number of bacterial colonies on the solid culture media that are visible to the naked eye. However, it is a time-consuming and laborious professional activity. Addressing the automation of colony counting by convolutional neural networks in our work, we have cultured 24 bacteria species of veterinary importance with different concentrations on solid media. A total of 56,865 colonies were annotated manually by bounding boxes on the 369 digital images of bacterial cultures. The published dataset will help developments that use artificial intelligence to automate the counting of bacterial colonies.
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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.