A Deep De-noising Neural Network to Mitigate Noise in Computed Tomography Images

Mohammad Fareeda Madeen Gouher; and Md Baig Mohammad

doi:https://doi.org/10.51976/ijari.822011

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A Deep De-noising Neural Network to Mitigate Noise in Computed Tomography Images

Mohammad Fareeda Madeen Gouher, and Md Baig Mohammad

Vol 8 , Issue 2 , April - June 2020 | Pages: 64-69 | Research Paper

https://doi.org/10.51976/ijari.822011

Received: January 17, 2020 | Revised: May 20, 2020 | Accepted: May 28, 2020 | Published Online: June 15, 2020

Author Details ( * ) denotes Corresponding author

1. * Mohammad Fareeda Madeen Gouher, Department of Electronics and communication Engineering, Prasad V Potluri Institute of Technology, Vijayawada, Andhra Pradesh, India (fareedam467@gmail.com)

2. and Md Baig Mohammad, Department of Electronics and communication Engineering, Andhra Loyola Institute of Engineering and Technology Vijayawada, Vijayawada, Andhra Pradesh, India

Liver cancer is a cause of high mortality worldwide. Early detection can save lives and help the medical practitioners to plan the treatment. Blood tests, biopsy and imaging techniques like CT, MRI are widely used in the diagnosis of the liver cancer.Noise contaminated CT Image suffers with low resolution and often leads to wrong diagnosis which may lead to improper treatment.Deep denoising Neural Network is used to mitigate the effects of noise on CT images. Deep denoising neural network outperform other filtering methods like mean, median, wiener filters on CT images of liver from cancer imaging archive (TCIA)

Keywords

Hepatocellular carcinoma; Weiner filter; Lucy-richardsonfilter; Deep denoising neural network

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