Hybrid convolutional neural network-transformer models for liver tumor segmentation: a comprehensive review.
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| Title: | Hybrid convolutional neural network-transformer models for liver tumor segmentation: a comprehensive review. |
|---|---|
| Authors: | Attiya, Ibrahim Mohamed1 im1959@fayoum.edu.eg, Thabet, Mostafa1 mtm00@fayoum.edu.eg, Kaseb, Mostafa R.2 mrk00@fayoum.edu.eg |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1382-1398. 17p. |
| Subjects: | Transformer models, Liver tumors, Artificial intelligence, Computer-assisted image analysis (Medicine), Machine learning, Liver disease diagnosis, Deep learning |
| Abstract: | Liver cancer is a major cause of cancer deaths worldwide, and early and accurate segmentation of liver tumors is a critical step in cancer diagnosis and treatment. However, existing image segmentation techniques have difficulty handling the variability of liver tumors on different image modalities. The emergence of deep learning (DL) and the development of convolutional neural networks (CNNs) have revolutionized image segmentation techniques. However, CNNs have limitations in handling long-range dependencies, which is a critical requirement for tumor segmentation. To overcome these limitations, researchers have proposed hybrid deep learning architectures, which combine CNNs and attention mechanisms or transformers, to integrate local and global information for image segmentation. In this paper, we provide a comprehensive and analytical review of over 50 state-of-the-art deep learning architectures for liver and tumor segmentation. In addition, we provide an extensive evaluation of 38 hybrid and advanced architectures for liver tumor segmentation and a comprehensive discussion of hybrid CNNtransformer architectures. We propose a novel multi-dimensional taxonomy and evaluate the state-of-the-art architectures on various dimensions, including architectural innovation, segmentation accuracy, computational efficiency, and clinical applicability using benchmark datasets such as LiTS and 3DIRCADb. In our critical evaluation of the state-of-the-art architectures, we identify some of the limitations and challenges of existing research and propose a unified evaluation framework and future research directions on self-supervised learning, explainable artificial intelligence (XAI), federated learning, and lightweight architectures. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194285626 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid convolutional neural network-transformer models for liver tumor segmentation: a comprehensive review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Attiya%2C+Ibrahim+Mohamed%22">Attiya, Ibrahim Mohamed</searchLink><relatesTo>1</relatesTo><i> im1959@fayoum.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Thabet%2C+Mostafa%22">Thabet, Mostafa</searchLink><relatesTo>1</relatesTo><i> mtm00@fayoum.edu.eg</i><br /><searchLink fieldCode="AR" term="%22Kaseb%2C+Mostafa+R%2E%22">Kaseb, Mostafa R.</searchLink><relatesTo>2</relatesTo><i> mrk00@fayoum.edu.eg</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1382-1398. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Liver+tumors%22">Liver tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Liver+disease+diagnosis%22">Liver disease diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Liver cancer is a major cause of cancer deaths worldwide, and early and accurate segmentation of liver tumors is a critical step in cancer diagnosis and treatment. However, existing image segmentation techniques have difficulty handling the variability of liver tumors on different image modalities. The emergence of deep learning (DL) and the development of convolutional neural networks (CNNs) have revolutionized image segmentation techniques. However, CNNs have limitations in handling long-range dependencies, which is a critical requirement for tumor segmentation. To overcome these limitations, researchers have proposed hybrid deep learning architectures, which combine CNNs and attention mechanisms or transformers, to integrate local and global information for image segmentation. In this paper, we provide a comprehensive and analytical review of over 50 state-of-the-art deep learning architectures for liver and tumor segmentation. In addition, we provide an extensive evaluation of 38 hybrid and advanced architectures for liver tumor segmentation and a comprehensive discussion of hybrid CNNtransformer architectures. We propose a novel multi-dimensional taxonomy and evaluate the state-of-the-art architectures on various dimensions, including architectural innovation, segmentation accuracy, computational efficiency, and clinical applicability using benchmark datasets such as LiTS and 3DIRCADb. In our critical evaluation of the state-of-the-art architectures, we identify some of the limitations and challenges of existing research and propose a unified evaluation framework and future research directions on self-supervised learning, explainable artificial intelligence (XAI), federated learning, and lightweight architectures. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i3.pp1382-1398 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1382 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Liver tumors Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Liver disease diagnosis Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Hybrid convolutional neural network-transformer models for liver tumor segmentation: a comprehensive review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Attiya, Ibrahim Mohamed – PersonEntity: Name: NameFull: Thabet, Mostafa – PersonEntity: Name: NameFull: Kaseb, Mostafa R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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