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Prediction of Tuberculosis using U-Net and segmentation techniques

arXiv 2104.01071 ↗First posted 2 Apr 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

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References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487

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