[BAO01-P15] Screening candidates of unicellular organisms in microscopic images using machine learning
Keywords:microscopic images, cell, machine learning
This study uses the microscopic images of Life Detection Microscope (LDM)  as experimental data. The microscopic images of LDM are subjected to fluorescent dyeing treatment. The microscopic image is E.coli with reference to the density of microbes in Atacama Desert . There are sparsely populated cells in the image, thus it is suitable to recognize each cell. Since cells are subjected to fluorescent staining treatment, it is easy to distinguish cells from others.
As a screening approach, machine learning using Convolutional Neural Network (CNN) , which is generally considered effective for image recognition, is used.
The CNN in this study is made in order to classify the cell in LDM’s microscopic image. The development language is Python , since Python has abundant machine learning frameworks. This study uses TensorFlow  as a framework. TensorFlow is a license-free machine learning library provided by Google.
Consequently, CNN was a valid approach in identification of the cell, but it cannot classify the focused cell or not.
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