Significance: Differentiation of primary central nervous system lymphoma from glioblastoma is clinically crucial to minimize the risk of treatments, but current imaging modalities often misclassify glioblastoma and lymphoma. Therefore, there is a need for methods to achieve high differentiation power intraoperatively.
Aim: The aim is to develop and corroborate a method of classifying normal brain tissue, glioblastoma, and lymphoma using optical coherence tomography with deep learning algorithm in an ex vivo experimental design.
Approach: We collected tumor specimens from ordinal surgical operations and measured them with optical coherence tomography. An attention ResNet deep learning model was utilized to differentiate glioblastoma and lymphoma from normal brain tissues.
Results: Our model demonstrated a robust classification power of detecting tumoral tissues from normal tissues and moderate discrimination between lymphoma and glioblastoma. Moreover, our results showed good consistency with the previous histological findings in the pathological manifestation of lymphoma, and this could be important from the aspect of future clinical practice.
Conclusion: We proposed and demonstrated a quantitative approach to distinguish different brain tumor types. Using our method, both neoplasms can be identified and classified with high accuracy. Hopefully, the proposed method can finally assist surgeons with decision-making intraoperatively.
In this study, we combined optical technology and machine learning to classify dental problems.We took totally 16 dental samples and 79 OCT images including 32 dental calculus(CA) images and 47 normal (HC) images. After image processing, we obtained optical attenuation coefficient, surface roughness and spectral information, and we put these features into two layer neural networks for training. We divided the data into training (24 CA / 37 HC = 61 total) and test (8 CA / 10 HC = 18 total) data, and the training data was checked with 10-fold cross validation to confirm no over-trained. The results showed that the model validity is 78%, and the test results have a sensitivity of 86%, specificity of 100%, and total accuracy of 94%.
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