mlsanityData to 7 Oct 2026

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

arXiv 2108.00358 ↗First posted 1 Aug 2021cs.CV cs.AIcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 196 references that are in the map. First posted on arXiv 1 Aug 2021.

Builds on

30 of 196 references.

References · 30

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18 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 9; 2023: 3; 2024: 1.

14 papers.

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  1. 2022EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Taskscited by 15
  2. 2022Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Imagescited by 8
  3. 2023Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectivescited by 6
  4. 2022Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networkscited by 5
  5. 2021EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluationcited by 2
  6. 2022Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trendcited by 2
  7. 2023PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhagecited by 2
  8. 2022A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurementscited by 1
  9. 2022An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism imagescited by 1
  10. 2022Artificial Neural Networks for Finger Vein Recognition: A Surveycited by 1
  11. 2022Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Featurescited by 1
  12. 2023ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regionscited by 1
2 more
  1. 2022Reconstructing high-order sequence features of dynamic functional connectivity networks based on diversified covert attention patterns for Alzheimer's disease classificationcited by 0
  2. 2024Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Datacited by 0