A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics
Integrating Deep Learning (DL) into abdominal imaging represents a significant leap forward in diagnosing and managing abdominal conditions, offering the potential to transform conventional medical practices. This comprehensive survey explores the application of DL techniques, such as Convolutional...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10982224/ |
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| author | Mariem Bellal Sanaa El Fkihi Korhan Cengiz Nikola Ivkovic |
| author_facet | Mariem Bellal Sanaa El Fkihi Korhan Cengiz Nikola Ivkovic |
| author_sort | Mariem Bellal |
| collection | DOAJ |
| description | Integrating Deep Learning (DL) into abdominal imaging represents a significant leap forward in diagnosing and managing abdominal conditions, offering the potential to transform conventional medical practices. This comprehensive survey explores the application of DL techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN), across various domains of abdominal imaging, including liver, spleen, kidney, and other structures such as subcutaneous adipose tissue (SAT), muscle, viscera, and bone. It discusses the critical role of performance metrics in evaluating model efficacy and clinical applicability. Furthermore, the paper highlights emerging trends in DL, such as integrating multimodal data and exploring unsupervised and semi-supervised learning techniques, which promise to address current limitations and pave the way for future advancements. Ethical considerations, including algorithmic bias, transparency in model development, and equitable patient care, are thoroughly examined to underscore the importance of ethical practices in deploying Artificial Intelligence (AI) technologies in healthcare. |
| format | Article |
| id | doaj-art-365d4efb37aa4bb8a47b8fb6058dd807 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-365d4efb37aa4bb8a47b8fb6058dd8072025-08-20T02:15:35ZengIEEEIEEE Access2169-35362025-01-0113798947991410.1109/ACCESS.2025.356662710982224A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance MetricsMariem Bellal0https://orcid.org/0009-0008-4771-4082Sanaa El Fkihi1https://orcid.org/0000-0002-5255-4406Korhan Cengiz2https://orcid.org/0000-0001-6594-8861Nikola Ivkovic3https://orcid.org/0000-0003-1730-2518Information Retrieval and Data Analytics Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat, MoroccoInformation Retrieval and Data Analytics Laboratory, ENSIAS, Mohammed V University in Rabat, Rabat, MoroccoDepartment of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi ArabiaFaculty of Organization and Informatics, University of Zagreb, Varaždin, CroatiaIntegrating Deep Learning (DL) into abdominal imaging represents a significant leap forward in diagnosing and managing abdominal conditions, offering the potential to transform conventional medical practices. This comprehensive survey explores the application of DL techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN), across various domains of abdominal imaging, including liver, spleen, kidney, and other structures such as subcutaneous adipose tissue (SAT), muscle, viscera, and bone. It discusses the critical role of performance metrics in evaluating model efficacy and clinical applicability. Furthermore, the paper highlights emerging trends in DL, such as integrating multimodal data and exploring unsupervised and semi-supervised learning techniques, which promise to address current limitations and pave the way for future advancements. Ethical considerations, including algorithmic bias, transparency in model development, and equitable patient care, are thoroughly examined to underscore the importance of ethical practices in deploying Artificial Intelligence (AI) technologies in healthcare.https://ieeexplore.ieee.org/document/10982224/Deep learning in medical imagingtraumatic abdominal injuriesmedical image analysisclinical decision support systemartificial intelligence |
| spellingShingle | Mariem Bellal Sanaa El Fkihi Korhan Cengiz Nikola Ivkovic A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics IEEE Access Deep learning in medical imaging traumatic abdominal injuries medical image analysis clinical decision support system artificial intelligence |
| title | A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics |
| title_full | A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics |
| title_fullStr | A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics |
| title_full_unstemmed | A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics |
| title_short | A Comprehensive Survey on Deep Learning in Abdominal Imaging: Datasets, Techniques, and Performance Metrics |
| title_sort | comprehensive survey on deep learning in abdominal imaging datasets techniques and performance metrics |
| topic | Deep learning in medical imaging traumatic abdominal injuries medical image analysis clinical decision support system artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10982224/ |
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