Free Image Recognition System for Research Use
If we talk about a tool that has boosted studies and research, it has been artificial intelligence, every time the demand for technologies based on AI increases, regardless of the branch that the industry develops, although we find on the other hand, many investigations have been slowed down by lack of resources or lack of technological tools that allow them to advance.
Thinking about overcoming these barriers, a group of engineers, computer scientists, biologists and the scientific community in general, researchers from the University Carlos III of Madrid, Instituto de Investigación Sanitaria Gregoria Marañon, as well as a team of collaborators from Switzerland and Sweden, have consolidated their efforts to develop an image recognition tool based on an artificial intelligence system, open source and free access.
This system is called deepImageJ, and is designed to process and analyze images, such as those obtained by microscopes, either to improve the quality of these, identify and classify elements within the images, among other options.
Developing a system of this magnitude has not been an easy task, it requires time and many financial resources, so small and low-budget researchers cannot access similar systems during their research.
In order to develop a similar system, it is necessary to prepare a computer that executes the requested tasks by exposing it to numerous samples of similar data. This base allows the development of deep learning models, so the more the system is exposed to more information, the more accurately the system is able to identify the data needed for the research.
deepImageJ is a complex, pre-trained system, allowing you to give commands and configure functions with a few clicks. Released for free and with an open license code, allowing the scientific community to get involved in its development and improvement, which translates into a project with a certain guarantee of sustainability.
In addition to being a free system, the web portal has a tutorial section, where you can get information to make the most of all the benefits it offers.
To learn more about the project, visit https://deepimagej.github.io/deepimagej/index.html
