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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 109
Article Submitted : 16127
Article Published : 7097
Total Authors : 19084
Total Reviewer : 688
Total Countries : 125
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| Paper Title: | Fully Convolutional Network and UNet for Lung Segmentation |
| Authors Name: | Humera Shaziya , Prof Shyamala Kattula |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_183020
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| Published Paper Id: | IJRTi2207101 |
| Published In: | Volume 7 Issue 7, July-2022 |
| DOI: | |
| Abstract: | Lung segmentation has been an active area of research to study the methods on effective segregation of lungs parenchyma. When the thoracic CT image is focused to investigate the lung conditions, there is no need to process the adjacent tissues of lungs. Therefore lung region can be extracted from the CT image and can provide as input to subsequent operations. Apparently lung segmentation can be considered as the prerequisite step to examine whether there are any lung nodules that can be benign or malignant. Several methods have been proposed. Conventional techniques deal with the pixel values and perform operations on those values. However the recent developments in the area of machine learning and deep learning have remarkable impact on the accuracy of separating the image into distinctive parts. The proposed work has presented two deep learning models for effectively segmenting the lung fields. The first one is fully convolutional network (FCN) and the second one is UNet. The FCN model has achieved the dice coefficient of 78.17% and UNet resulted in 98.13% dice coefficient. Therefore UNet has been an appropriate model for segmentation and showed better results compared to FCN. |
| Keywords: | Lung Segmentation, Deep Learning, Fully Convolutional Network, UNet |
| Cite Article: | "Fully Convolutional Network and UNet for Lung Segmentation", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.702 - 708, July-2022, Available :http://www.ijrti.org/papers/IJRTi2207101.pdf |
| Downloads: | 000204932 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTi2207101
Registration ID:183020
Published In: Volume 7 Issue 7, July-2022
DOI (Digital Object Identifier):
Page No: 702 - 708 Country: Hyderabad, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTi2207101 Published Paper PDF: https://www.ijrti.org/papers/IJRTi2207101 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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