Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies
A special issue of Biomedicines (ISSN 2227-9059).
Deadline for manuscript submissions: 30 April 2025 | Viewed by 39
Special Issue Editor
Interests: AI-driven diagnostic tools; biomedical artificial intelligence; cancer detection algorithms; convolutional neural networks (CNNs); deep learning in healthcare; healthcare data analysis; interdisciplinary healthcare research; medical imaging analysis; precision medicine; transformer models in diagnostics
Special Issue Information
Dear Colleagues,
This Special Issue, titled “Advances in Brain Tumor Diagnosis: Innovations and Emerging Technologies”, will collate cutting-edge research and comprehensive reviews focused on novel diagnostic methods for brain tumors. With the increasing application of artificial intelligence, machine learning, and advanced imaging technologies, the Special Issue aims to highlight the progress made in early detection, classification, and prognostication of brain tumors, including gliomas, meningiomas, and other neuro-oncological conditions.
We invite submissions that explore the integration of deep learning, medical imaging analysis, predictive modeling, and multi-modal data fusion for precise and reliable diagnostics. Topics may include (but are not limited to) innovative AI architectures, clinical data integration, and digital pathology approaches that enhance the accuracy of diagnosis and supports clinicians in their decision making. Additionally, contributions addressing challenges such as data scarcity, model interpretability, and clinical translation are highly welcome. This Special Issue aspires to provide a comprehensive overview of current advancements and offer a roadmap for future breakthroughs in brain tumor diagnostics, ultimately supporting better patient outcomes and personalized treatment pathways.
Dr. Mubarak Taiwo Mustapha
Guest Editor
Manuscript Submission Information
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Keywords
- brain tumor diagnosis
- artificial intelligence in oncology
- deep learning in medical imaging
- machine learning models
- predictive analytics in healthcare
- digital pathology
- glioma detection
- multi-modal data integration
- neuro-oncology
- precision medicine
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