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

TL;DR

This comprehensive review discusses how artificial neural networks, from traditional multilayer perceptrons to modern convolutional neural networks and visual transformers, are applied to microorganism image analysis, highlighting challenges and future directions.

Contribution

It provides a detailed overview of neural network applications in microorganism image analysis, including development, methodologies, and potential future research directions.

Findings

Neural networks improve microorganism image analysis efficiency.

Deep learning methods address challenges like feature insignificance and under-segmentation.

The review identifies future research directions in this field.

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.