Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.
Biomed Opt Express
; 15(4): 2343-2357, 2024 Apr 01.
Article
en En
| MEDLINE
| ID: mdl-38633066
ABSTRACT
In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.
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MEDLINE
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En
Revista:
Biomed Opt Express
Año:
2024
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Article
País de afiliación:
Taiwán