Lung CT image Segmentation Using Pix2Pix Model

Authors

  • Vanita D. Jadhav
  • Lalit V. Patil

DOI:

https://doi.org/10.47750/pnr.2022.13.S09.295

Keywords:

Deep learning; Lung Segmentation; Lung cancer;lung segmentation map; lung CT image; pulmonary nodule.

Abstract

An important first phase in the computerized diagnosis of the lung computed tomography(CT) scan images is automatic lung segmentation. However, current techniques do not precisely segment the lung in the presence of dense irregularities. This research proposes an improved lung segmentation accuracy approach based on generative adversarial networks (GANs).Effective segmentation of the lung area from the nearby chest area is achieved by proposed network. In order to achieve image segmentation, we used Pix2Pix model and took usage of their ability to convert images. With the use of generative adversarial networks, the unique lung CT image was transformed into the segmented image. Investigational study makes use of the ILD dataset that is openly accessible. The experimental study demonstrates that the projected Pix2Pix model performance is unaffected by the existence of compact irregularities in lung CT.

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Published

2022-11-23

How to Cite

Vanita D. Jadhav, & Lalit V. Patil. (2022). Lung CT image Segmentation Using Pix2Pix Model. Journal of Pharmaceutical Negative Results, 2485–2493. https://doi.org/10.47750/pnr.2022.13.S09.295

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Section

Articles