Segmentation of the Lungs on X-Ray Thorax Image with CNN Architecture U-Net

Lungs are one of the most important parts of the human body. They are very susceptible to various disorders and diseases. For this reason, it is necessary to detect or diagnose the lungs. In this study, we present a method for lung segmentation using the CNN method U-Net architecture. The initial st...

Full description

Saved in:
Bibliographic Details
Published in:Computer engineering and applications journal 2022-01, Vol.11 (2), p.101-111
Main Authors: Pranata, Teddi, Desiani, Anita, Suprihatin, Bambang, Hanum, Herlina, Efriliyanti, Filda
Format: Article
Language:English
Subjects:
Equalization
Histograms
Image contrast
Image segmentation
Lungs
Performance evaluation
Thorax
Websites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Lungs are one of the most important parts of the human body. They are very susceptible to various disorders and diseases. For this reason, it is necessary to detect or diagnose the lungs. In this study, we present a method for lung segmentation using the CNN method U-Net architecture. The initial stage was preprocessed did a 1-1 correspondence to equalize the amount of training data and testing data and resized the image so all images have the same size. The process continued with the CLAHE (Contrast Limited Adaptive Histogram Equalization), and after that, the segmentation process was carried out according to the method. This study used a dataset from the Kaggle website. The results used the CNN method of the U-Net architecture in data get an average accuracy of 91.68%, sensitivity 92.80%, and specificity 89.15%, precision 95.07, and F1-Score 93. 92%. Based on the performance evaluation results, it was concluded that the method proposed in the study is great and valid in the lungs segmentation on X-Ray Thorax images.
ISSN:2252-4274
2252-5459