Leveraging Pretrained Vision Transformers for Automated Cancer Diagnosis in Optical Coherence Tomography Images

This study presents a novel approach to brain cancer detection based on Optical Coherence Tomography (OCT) images and advanced machine learning techniques. The research addresses the critical need for accurate, real-time differentiation between cancerous and noncancerous brain tissue during neurosurgical procedures. The proposed method combines a pre-trained Vision Transformer (ViT) model, specifically DiNOV2, with a convolutional neural network (CNN) operating on Grey Level Co-occurrence Matrix (GLCM) texture features. This dual-path architecture leverages both the global context capture capabilities of transformers and the local texture analysis strengths of GLCM + CNNs. The dataset comprised OCT images from 11 patients, with 5,831 B-frame slices used for training and validation, and 1,610 slices for testing. The model achieved high accuracy in distinguishing cancerous from noncancerous tissue, with 99.7% accuracy on the training dataset, 99.4% on the validation dataset, and 94.9% accuracy on the test dataset. This approach demonstrates significant potential for achieving and improving intraoperative decision-making in brain cancer surgeries, offering real-time, high-accuracy tissue classification and surgical guidance. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors acknowledge the funding support in part by NIH under a grant No. R01CA200399 (Li). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data obtained at the Johns Hopkins Hospital under an approved Institutional Review Board protocol I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors