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.
Cite
@article{arxiv.2104.01071,
title = {Prediction of Tuberculosis using U-Net and segmentation techniques},
author = {Dennis Núñez-Fernández and Lamberto Ballan and Gabriel Jiménez-Avalos and Jorge Coronel and Patricia Sheen and Mirko Zimic},
journal= {arXiv preprint arXiv:2104.01071},
year = {2021}
}
Comments
AI for Public Health Workshop at ICLR 2021. arXiv admin note: text overlap with arXiv:2007.02482