It is necessary to have a precise evaluation of the morphometry of a
kidney tumor in make decisions regarding treatment and diagnosis
accurately. It is difficult to undertake quantitative research between
kidney tumor morphology and clinical outcomes because there is a lack
of data and the need to manually evaluate imaging parameters, which
is a lengthy process. A conventional generative adversarial network
model serves as the foundation for the STGAN approach, which is an
autonomous kidney segmentation method that is proposed in this
research as a solution to this problem. The primary foundation of this
system is comprised of a completely convolutional generating network
that is made up of densely linked blocks and a discriminator network
that is equipped with multi-scale feature extraction as well. For
conducting quantitative and qualitative comparisons with the STGAN
methodology, the medical image segmentation networks U-Net, FCN,
and SegAN are deployed. STGAN achieves greater performance in
comparison to the other neural networks, the model that we have
proposed demonstrates potential for improving the accuracy of CT-
based kidney segmentation.
K. Sumathi, P. Karthika
Erode Sengunthar Engineering College, India
GAN, Diagnosis, Kidney Disease, Segmentation
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| Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 5 , Issue: 3 , Pages: 655 - 658 )
Date of Publication :
June 2024
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148
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