Prediction of Tuberculosis using U-Net and segmentation techniques
This work addresses the public health problem of tuberculosis diagnosis, particularly in Peru, by providing an automated method that is easier to use by untrained personnel, though it appears incremental as it applies an existing U-Net method to a new dataset.
The researchers tackled automating tuberculosis diagnosis by using a U-Net network to segment cord-shaped bacterial accumulations from lens-free microscopy images, achieving good accuracy for TB prediction.
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.