Classification of objects under a microscope using neural networks: where and how it is applied
Neural networks are able to cluster, detect and segment objects under a microscope and other means of magnification. It helps in agriculture, manufacturing, engineering, medicine and other areas where research is being done.
Defect detection
A neural network can be trained to recognize anomalies in images and identify potential defects. In manufacturing, this is used to detect and classify defects in alloys or chips.
A neural network can be trained to recognize anomalies in images and identify potential defects. In manufacturing, this is used to detect and classify defects in alloys or chips.
Identification and counting in production
Neural networks define and count objects without human intervention. It helps to analyze the number of blood cells, molecules, bacteria and other indicators in the sample.
Neural networks define and count objects without human intervention. It helps to analyze the number of blood cells, molecules, bacteria and other indicators in the sample.
Comparison
With the help of neural networks, it is possible to compare changes in the same object over time, for example, a change in the density of a material under the influence of a reagent.
With the help of neural networks, it is possible to compare changes in the same object over time, for example, a change in the density of a material under the influence of a reagent.
Automation of any research
Working with a microscope is laborious and harmful to posture as well as to people's vision. Due to stress and fatigue, errors can occur in studies.
The neural network automatically distinguishes, segments and counts objects in a microscopic image. This helps to automate manual labor, reduce research time and costs, and improve accuracy by several times.
Working with a microscope is laborious and harmful to posture as well as to people's vision. Due to stress and fatigue, errors can occur in studies.
The neural network automatically distinguishes, segments and counts objects in a microscopic image. This helps to automate manual labor, reduce research time and costs, and improve accuracy by several times.
Recognition
Neural networks divide cells by type, for example, into cancerous and normal. Cells are segmented by phase in the cell cycle to find damaged ones. Separate bacteria from erythrocytes and solve other problems automatically.
Neural networks divide cells by type, for example, into cancerous and normal. Cells are segmented by phase in the cell cycle to find damaged ones. Separate bacteria from erythrocytes and solve other problems automatically.
