Rani Priya, Kotwal Shallu, Manhas Jatinder, Sharma Vinod, Sharma Sparsh
Computer Science and IT, University of Jammu, Jammu, India.
Information Technology, Baba Ghulam Shah Badshah University, Rajouri, India.
Arch Comput Methods Eng. 2022;29(3):1801-1837. doi: 10.1007/s11831-021-09639-x. Epub 2021 Aug 31.
Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.
微生物构成了地球上大部分的生物多样性，对人类生活极为重要。它们也是生态系统过程中不可或缺的一部分。识别它们的过程非常繁琐，但在微生物学中对于进行不同实验至关重要。为了克服某些挑战，机器学习技术帮助微生物学家实现整个过程的自动化。本文对使用机器学习（ML）和深度学习技术进行不同微生物图像识别的研究进行了系统综述。该综述研究了某些研究问题，以分析一段时间内有关图像预处理、特征提取、分类技术、评估措施、方法局限性和技术发展的研究。除此之外，本文还探讨了该领域研究人员面临的某些挑战。在1995年至2021年期间，共考虑了按出现时间顺序排列的100篇研究出版物。由于对不同方法的详细分析以及对不同ML技术在微生物图像识别领域应用效果的全面概述，本综述将对研究人员极为有益。
