New approach to image segmentation: U-Net Convolutional Network for Multiresolutioan CT Image Lung Segementation

Surono, Sugiyarto and Irsalinda, Nursyiva (2023) New approach to image segmentation: U-Net Convolutional Network for Multiresolutioan CT Image Lung Segementation. Emerging Science Journal, 7 (2). ISSN ISSN: 2610-9182

Text (SIMILARITY CEK) similaritas ESJ 1444.pdf - Accepted Version Download (2MB)

Abstract

Image processing is the main topic of discussion in the field of computer vision technology. With
the increase in the number of images used over time, the types of images with different resolution
qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image
processing. Therefore, a method with high performance is needed for image processing. In image
processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation
of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later
produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT
image dataset, which has different resolutions in each image, to segment the image that produces a
segmented lung image. In this study, we conducted experiments for many training and testing data
ratios while also comparing the model performances between the single resolution dataset and the
multiresolution dataset. The results showed that the segmentation accuracy using a single resolution
dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For
the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to
1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio
is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for
multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and
9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single
resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio.

Item Type:	Artikel Umum
Subjects:	Q Science > QA Mathematics
Divisi / Prodi:	Faculty of Applied Science and Technology (Fakultas Sains Dan Teknologi Terapan) > S1-Mathematics (S1-Matematika)
Depositing User:	Dr Sugiyarto Surono
Date Deposited:	14 Jun 2023 01:54
Last Modified:	14 Jun 2023 01:54
URI:	http://eprints.uad.ac.id/id/eprint/43374

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