InNetGAN: Inception network-based generative adversarial network for denoising low-dose computed tomography

Kulathilake, K. A. Saneera Hemantha and Abdullah, Nor Aniza and Bandara, A. M. Randitha Ravimal and Lai, Khin Wee (2021) InNetGAN: Inception network-based generative adversarial network for denoising low-dose computed tomography. Journal of Healthcare Engineering, 2021. ISSN 2040-2295, DOI

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Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient's risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inception network modules (InNetGAN) as a solution for filtering the noise transmission through skip connections and preserving the texture and fine structure of LDCT images. The proposed Generator is modeled based on the U-net architecture. The skip connections in the U-net architecture are modified with three different inception network modules to filter out the noise in the feature maps passing over them. The quantitative and qualitative experimental results have shown the performance of the InNetGAN model in reducing noise and preserving the subtle structures and texture details in LDCT images compared to the other state-of-the-art denoising algorithms.

Item Type: Article
Uncontrolled Keywords: Low-dose Computed Tomography; Generative Adversarial Network; Inception network; InNetGAN
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology > Department of Computer System & Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 10 Mar 2022 02:30
Last Modified: 10 Mar 2022 02:30

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