Classifying breast cancer in ultrahigh-resolution optical coherence tomography images using convolutional neural networks

Optical coherence tomography (OCT) is being investigated in breast cancer diagnostics as a real-time histology evaluation tool. We present a customized deep convolutional neural network (CNN) for classification of breast tissues in OCT B-scans. Images of human breast samples from mastectomies and breast reductions were acquired using a custom ultrahigh-resolutionOCTsystem with 2.72 mu m axial resolution and 5.52 mu m lateral resolution. The network achieved 96.7% accuracy, 92% sensitivity, and 99.7% specificity on a dataset of 23 patients. The usage of deep learning will be important for the practical integration ofOCTinto clinical practice. (C) 2022 Optica Publishing Group

Automated summary

This article presents a customized deep convolutional neural network (CNN) for the classification of breast tissues in optical coherence tomography (OCT) scans, achieving high accuracy and specificity in breast cancer diagnostics.