# 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: https://arxiv.org/abs/2108.00358 · HTML: https://mlsanity.com/paper/2108.00358/

## Key facts

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

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks](https://mlsanity.com/paper/2003.12255/) (2020)
- [A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches](https://mlsanity.com/paper/2103.13625/) (2021)
- [Gastric histopathology image segmentation using a hierarchical conditional random field](https://mlsanity.com/paper/2003.01302/) (2020)
- [Applying Faster R-CNN for Object Detection on Malaria Images](https://mlsanity.com/paper/1804.09548/) (2018)
- [A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis](https://mlsanity.com/paper/2102.12147/) (2021)
- [A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation](https://mlsanity.com/paper/2003.03744/) (2020)
- [A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers](https://mlsanity.com/paper/2107.07699/) (2021)
- [EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks](https://mlsanity.com/paper/2102.10370/) (2021)
- [A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Features](https://mlsanity.com/paper/2004.03416/) (2020)

## Built on by (most cited first, 14 of 14)

- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images](https://mlsanity.com/paper/2212.12844/) (2022)
- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks](https://mlsanity.com/paper/2207.07173/) (2022)
- [EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation](https://mlsanity.com/paper/2110.07723/) (2021)
- [Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend](https://mlsanity.com/paper/2207.02148/) (2022)
- [PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage](https://mlsanity.com/paper/2308.10521/) (2023)
- [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://mlsanity.com/paper/2202.09020/) (2022)
- [An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images](https://mlsanity.com/paper/2204.01341/) (2022)
- [Artificial Neural Networks for Finger Vein Recognition: A Survey](https://mlsanity.com/paper/2208.13341/) (2022)
- [Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features](https://mlsanity.com/paper/2208.14957/) (2022)
- [ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions](https://mlsanity.com/paper/2308.08313/) (2023)
- [Reconstructing high-order sequence features of dynamic functional connectivity networks based on diversified covert attention patterns for Alzheimer's disease classification](https://mlsanity.com/paper/2211.11750/) (2022)
- [Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data](https://mlsanity.com/paper/2409.07942/) (2024)

## 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.