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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Muhammad Awaludin Djohar
Universitas Sriwijaya
Computer Science & IT Engineering

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Liver Segmentation Using Convolutional Neural Network Method with U-Net Architecture Muhammad Awaludin Djohar; Anita Desiani; Ali Amran; Sugandi Yahdin; Dewi Lestari Dwi Putri; Des Alwine Zayanti; Novi Rustiana Dewi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.6751

Abstract

Abnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%.  The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders.
Co-Authors Ali Amran Anita Desiani Des Alwine Zayanti, Des Alwine Dewi Lestari Dwi Putri Novi Rustiana Dewi Sugandi Yahdin