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In Journal of biophotonics

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, non-invasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80 % for mouse skin. Using the sub-micron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications. This article is protected by copyright. All rights reserved.

Ho Chi-Jui, Calderon-Delgado Manuel, Chan Chin-Cheng, Lin Ming-Yi, Tjiu Jeng-Wei, Huang Sheng-Lung, Chen Homer H


Optical coherence tomography, computer-aided diagnosis, convolutional neural network, deep learning, squamous cell carcinomas