Deep Learning for Hyperspectral Data Analysis and Manipulation of Augmented Medical Data
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 2133
Special Issue Editor
Interests: early diagnosis through deep learning; data mining; machine learning; medical image analysis; data analysis; natural language processing
Special Issue Information
Dear Colleagues,
Deep Learning for document text analysis and manipulation of augmented medical data represents a remarkable convergence of cutting-edge technologies aimed at revolutionizing healthcare. This Special Issue focuses on the innovative application of deep learning algorithms in the processing of hyperspectral textual data and medical documents and manipulating augmented medical data.
Within this domain, deep learning models demonstrate unparalleled capabilities in extracting meaningful insights from vast amounts of unstructured medical text, such as clinical notes, tabular data, research papers, and patient records. These models employ advanced natural language processing techniques to accurately interpret and categorize textual data, enabling healthcare professionals to efficiently access relevant information for diagnosis, treatment planning, and research purposes.
Moreover, the integration of deep learning with augmented medical data introduces a new dimension to medical analysis and decision-making. Augmented medical data encompasses a variety of sources, including electronic health records, medical imaging, genomic data, and wearable sensor data. By using deep learning techniques, researchers can manipulate and analyze these augmented data to uncover hidden patterns, predict patient outcomes, personalize treatment strategies, and ultimately enhance the quality of patient care.
This Special Issue invites contributions that explore the latest advancements, challenges, and applications of deep learning for document text analysis and the manipulation of augmented medical data, offering valuable insights into the future of healthcare informatics.
Dr. Saad Bin Ahmed
Guest Editor
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Keywords
- hyperspectral data
- explainable AI
- augmented data
- deep learning
- medical report generation
- generative AI
- early diagnosis
- textual image analysis
- pattern identification
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