A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Abstract

Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

Parasitic egg recognition using convolution and attention network

“…On the other hand, deep learning-based algorithms have been increasingly popular in the previous decade as computer performance and the number of available image datasets have both improved 14 , 15 . Deep learning has demonstrated remarkable efficiency in a variety of disciplines, including text recognition, computer-assisted diagnosis, facial identification, and drug development 16 . Deep learning, particularly the Convolutional Neural Network (CNN), stimulates novel parasite classification research in the parasite egg detection task because of its promising performance and speed in object recognition 4 , 5 , 8 , 13 , 17 , 18 .…”