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; and Chia-Wei Sun

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

Author : Sanford P. C. Hsu, Miao-Hui Lin, Chun-Fu Lin, Tien-Yu Hsiao, Yi-Min Wang, and Chia-Wei Sun Journa Name: BIOMEDICAL OPTICS EXPRESS Country : USA Volume: 15 issue: 4 Year: 2024 Views : 220

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.