Deep learning transforms smartphone microscopes into laboratory-grade devices

UCLA

April, 2018
Deep learning transforms smartphone microscopes into laboratory-grade devices
Image of a blood smear from a cell phone camera (left), following enhancement by the algorithm (center), and taken by a lab microscope (right).

Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. The technique improves the resolution and color details of smartphone images so much that they approach the quality of images from laboratory-grade microscopes.

The technique uses attachments that can be inexpensively produced with a 3-D printer, at less than $100 a piece, versus the thousands of dollars it would cost to buy laboratory-grade equipment that produces images of similar quality.

Researchers developed an attachment that can be placed over the smartphone lens to increase the resolution and the visibility of tiny details of the images they take, down to a scale of approximately one micron.

The new technique compensates for the difference in quality between smartphone cameras’ image sensors and lenses and those of high-end lab equipment by using artificial intelligence to reproduce the level of resolution and color details needed for a laboratory analysis.

The research was led by Aydogan Ozcan, Chancellor’s Professor of Electrical and Computer Engineering and Bioengineering, and Yair Rivenson, a UCLA postdoctoral scholar. Ozcan’s research group has introduced several innovations in mobile microscopy and sensing, and it maintains a particular focus on developing field-portable medical diagnostics and sensors for resource-poor areas.

To see if their technique would work on other types of lower-quality images, the researchers used deep learning to successfully perform similar transformations with images that had lost some detail because they were compressed for either faster transmission over a computer network or more efficient storage.

To learn more about this project, click here.

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