1. Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography

    Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography

    In breast cancer, 20%–30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 ...

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