Back to search

Article

A NOVEL DEEP LEARNING MODEL, RDB CYCYLEGAN-CBAM FOR LOW-DOSE CT IMAGE DENOISING

2026-02-18

Abstract excerpt

Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cancer risk. Reducing CT radiation dose to improve patient safety inherently increases image noise and artifacts. Generative adversarial networks (GANs) have shown promise for unsupervised low-dose CT (LDCT) denoising. Building on this, RDBCycleGAN-CBAM, a CycleGAN-based...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
30507394-9e4e-5303-b7de-8bfe19d9f1ef
DOI
10.64898/2026.02.17.706311
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
A NOVEL DEEP LEARNING MODEL, RDB CYCYLEGAN-CBAM FOR LOW-DOSE CT IMAGE DENOISINGDOI 10.64898/2026.02.17.706311
Select a neighboring publication to make it the new centre.