Deep Learning-Based Microorganisms Classification Systems Using Microscopic Images
DOI:
https://doi.org/10.24237/ASJ.02.02.750BKeywords:
Microorganisms Classification, Pre-trained Convolutional Neural Networks (CNNs), Parasites, Fungi, Bacteria, Viruses, Microscopic image datasetsAbstract
With enormous amounts of microscopic images created in the research of microbiology, conventional methods of computation have turned more and more difficult in handling them. On the contrary, deep learning models be inclined to hold outstanding performance in speed and accuracy. Recently, the microbiologist's community embraced the deep learning models, thus leading to appearing new applications with unprecedented discoveries and perspectives in the research of microbiology. In this paper, various microorganisms classification systems are implemented using various types of microscopic image datasets including parasites, fungi, bacteria, and viruses. The backbone of these systems is pre-trained convolutional neural networks (CNNs) for detecting microorganisms. This study conducted an exhaustive analysis of various pre-trained CNNs models in the field of microorganism classification as well as their experimental design and validation and the future extent to present profound perception for the researchers active in this field.
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