Brain tumor grading diagnosis using transfer learning based on optical coherence tomography

Sanford P.C. Hsu, Miao Hui Lin, Chun Fu Lin, Tien Yu Hsiao, Yi Min Wang, Chia Wei Sun*

*此作品的通信作者

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2343-2357
頁數15
期刊Biomedical Optics Express
15
發行號4
DOIs
出版狀態Published - 1 4月 2024

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