A Multimodal Convolutional Neural Network Based Approach for DICOM Files Classification. , 2025(1), e70107.
In this study, we developed a convolutional neural network approach for directly classifying digital imaging and communication in medicine files in medical imaging applications. Existing models require converting this format into other formats like portable network graphics. This conversion leads to...
Saved in:
Main Authors: | Mabirizi, Vicent, Wasswa, William, Kawuma, Simon |
---|---|
Format: | Article |
Language: | English |
Published: |
wiley
2025
|
Subjects: | |
Online Access: | http://hdl.handle.net/20.500.12493/2925 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep Learning Techniques in DICOM Files Classification: A Systematic Review
by: Mabirizi, Vicent, et al.
Published: (2025) -
Diegif: An efficient and secured DICOM to EGIF conversion framework for confidentiality in machine learning training
by: Abdullah Al Siam, et al.
Published: (2025-03-01) -
Analyzing temporal imaging patterns in acute ischemic stroke via DICOM-timestamps
by: Alexander Rau, et al.
Published: (2025-01-01) -
Secured DICOM medical image transition with optimized chaos method for encryption and customized deep learning model for watermarking
by: R. Abirami, et al.
Published: (2025-04-01) -
Metadata functional requirements for genomic data practice and curation
by: Hong Huang, et al.
Published: (2024-06-01)