The effectiveness of FAT‐Net in chest X‐ray segmentation Article Swipe
Maedeh Sadat Khorasani
,
Farshid Babapour Mofrad
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/tje2.70065
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/tje2.70065
Objective : The segmentation of lung images is intricate due to the varied forms and distortions caused by the disease. One of the standard tools for checking and diagnosing lung diseases is CXR images. In this regard, the effectiveness of the FAT‐Net network for automatic segmentation of lung CXR images was investigated. To our knowledge, research has yet to employ the proposed network to segment lung CXR images. Materials and methods : The suggested network's performance was compared to two pre‐existing networks, Attention U‐Net and SegNet. Evaluations were performed on two public datasets named Montgomery and Shenzhen. After preprocessing the data, all three models were trained and evaluated. Results : The accuracy and Dice obtained for the proposed network are 98.12 and 96.10, respectively, and the average IOU index, which dominates the Attention U‐Net and SegNet networks, shows the stability and the suggested network outperforms previous networks reported in the literature for lung image segmentation and is more robust than the other two networks. Conclusion : According to the evaluations performed on the Montgomery and Shenzhen datasets, the proposed network has provided more accurate and reliable segmentation for lung CXR images by mastering the two networks above.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/tje2.70065
- OA Status
- gold
- Cited By
- 2
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407224149
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407224149Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/tje2.70065Digital Object Identifier
- Title
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The effectiveness of FAT‐Net in chest X‐ray segmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Maedeh Sadat Khorasani, Farshid Babapour MofradList of authors in order
- Landing page
-
https://doi.org/10.1049/tje2.70065Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1049/tje2.70065Direct OA link when available
- Concepts
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Preprocessor, Segmentation, Computer science, Artificial intelligence, Pattern recognition (psychology), Dice, Image segmentation, Data mining, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.distortions | 16 |
| abstract_inverted_index.evaluations | 170 |
| abstract_inverted_index.outperforms | 145 |
| abstract_inverted_index.performance | 76 |
| abstract_inverted_index.segmentation | 4, 46, 155, 187 |
| abstract_inverted_index.effectiveness | 39 |
| abstract_inverted_index.investigated. | 52 |
| abstract_inverted_index.preprocessing | 99 |
| abstract_inverted_index.respectively, | 124 |
| abstract_inverted_index.pre‐existing | 81 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.92323345 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |