  One of the most serious public health problems in Peru and worldwide is
Tuberculosis (TB), which is produced by a bacterium known as Mycobacterium
tuberculosis. The purpose of this work is to facilitate and automate the
diagnosis of tuberculosis using the MODS method and using lens-free microscopy,
as it is easier to calibrate and easier to use by untrained personnel compared
to lens microscopy. Therefore, we employed a U-Net network on our collected
data set to perform automatic segmentation of cord shape bacterial accumulation
and then predict tuberculosis. Our results show promising evidence for
automatic segmentation of TB cords, and thus good accuracy for TB prediction.
