Deep Learning Techniques in DICOM Files Classification: A Systematic Review
The digital imaging and communications in medicine (DICOM) format is a widely adopted standard for storing medical imaging data, integrating both image and metadata critical for clinical diagnostics. However, its complexity poses challenges for deep learning applications, particularly in extracting...
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| Format: | Article |
| Language: | English |
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BON VIEW PUBLISHING PTE.LTD.
2025
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| Online Access: | http://hdl.handle.net/20.500.12493/2902 |
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| author | Mabirizi, Vicent Kawuma, Simon Natumanya, Deborah Wasswa, William |
| author_facet | Mabirizi, Vicent Kawuma, Simon Natumanya, Deborah Wasswa, William |
| author_sort | Mabirizi, Vicent |
| collection | KAB-DR |
| description | The digital imaging and communications in medicine (DICOM) format is a widely adopted standard for storing medical imaging data, integrating both image and metadata critical for clinical diagnostics. However, its complexity poses challenges for deep learning applications, particularly in extracting and processing this dual-layered data. This review analyzes 23 peer-reviewed studies published between 2014 and 2024, sourced from PubMed, Google Scholar, PLOS, Science Direct, and IEEE databases. Guided by Arksey and O’Malley’s scoping methodology, the review reveals that existing deep learning techniques typically rely on converting DICOM images into simpler formats like JPEG, TIF, or PNG for classification, a process that often results in metadata loss and reduced classification accuracy. Frameworks such as MONAI, NVIDIA Clare, SimpleITK, and OpenCV facilitate direct DICOM processing but face limitations, including overfitting, challenges with data heterogeneity, and inefficiencies in handling large datasets. This review emphasizes the urgent need for developing a robust convolutional neural network architecture capable of directly processing DICOM data to preserve metadata integrity and enhance predictive performance, paving way for more reliable and scalable medical imaging solutions. |
| format | Article |
| id | oai:idr.kab.ac.ug:20.500.12493-2902 |
| institution | KAB-DR |
| language | English |
| publishDate | 2025 |
| publisher | BON VIEW PUBLISHING PTE.LTD. |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-29022025-04-03T00:00:50Z Deep Learning Techniques in DICOM Files Classification: A Systematic Review Mabirizi, Vicent Kawuma, Simon Natumanya, Deborah Wasswa, William DICOM image processing deep learning in radiology convolutional neural network medical imaging frameworks medical metadata preservation scalable image analysis models The digital imaging and communications in medicine (DICOM) format is a widely adopted standard for storing medical imaging data, integrating both image and metadata critical for clinical diagnostics. However, its complexity poses challenges for deep learning applications, particularly in extracting and processing this dual-layered data. This review analyzes 23 peer-reviewed studies published between 2014 and 2024, sourced from PubMed, Google Scholar, PLOS, Science Direct, and IEEE databases. Guided by Arksey and O’Malley’s scoping methodology, the review reveals that existing deep learning techniques typically rely on converting DICOM images into simpler formats like JPEG, TIF, or PNG for classification, a process that often results in metadata loss and reduced classification accuracy. Frameworks such as MONAI, NVIDIA Clare, SimpleITK, and OpenCV facilitate direct DICOM processing but face limitations, including overfitting, challenges with data heterogeneity, and inefficiencies in handling large datasets. This review emphasizes the urgent need for developing a robust convolutional neural network architecture capable of directly processing DICOM data to preserve metadata integrity and enhance predictive performance, paving way for more reliable and scalable medical imaging solutions. 2025-04-02T14:59:04Z 2025-04-02T14:59:04Z 2025 Article Mabirizi, V., Kawuma, S., Natumanya, D., & Wasswa, W. (2025). Deep Learning Techniques in DICOM Files Classification: A Systematic Review. 10.47852/bonviewAIA52024425 http://hdl.handle.net/20.500.12493/2902 en 00 Attribution 3.0 United States http://creativecommons.org/licenses/by/3.0/us/ application/pdf BON VIEW PUBLISHING PTE.LTD. |
| spellingShingle | DICOM image processing deep learning in radiology convolutional neural network medical imaging frameworks medical metadata preservation scalable image analysis models Mabirizi, Vicent Kawuma, Simon Natumanya, Deborah Wasswa, William Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title | Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title_full | Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title_fullStr | Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title_full_unstemmed | Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title_short | Deep Learning Techniques in DICOM Files Classification: A Systematic Review |
| title_sort | deep learning techniques in dicom files classification a systematic review |
| topic | DICOM image processing deep learning in radiology convolutional neural network medical imaging frameworks medical metadata preservation scalable image analysis models |
| url | http://hdl.handle.net/20.500.12493/2902 |
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