New mobile device identifies airborne allergens using deep learning
Tuesday, November 6, 2018 - 07:40
in Biology & Nature
UCLA researchers invented a portable device that uses holograms and machine learning to identify and measure airborne biological particles, or bioaerosols, that originate from living organisms such as plants or fungi. Trained to recognize five common allergens—pollen from Bermuda grass, oak, ragweed and spores from two types of mold—the system classified samples with an accuracy of 94 percent using deep learning.