# Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images

Source: [Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images](<https://www.sota2.com/research/paper/68fa67ea1e209131cea191cf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:25:00.051Z

Authors: Dennis N\\'u\\~nez-Fern\\'andez, Lamberto Ballan, Gabriel Jim\\'enez-Avalos, Jorge Coronel, Mirko Zimic

Published: 2020-07-06T00:36:44.000Z

## Abstract

Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for automatic segmentation of TB cords.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.02482>)

[PDF](<https://arxiv.org/pdf/2007.02482.pdf>)