Ho C.-JCalderon-Delgado MChan C.-CLin M.-YTjiu J.-WHuang S.-LHOMER H. CHENSHENG-LUNG HUANG2022-04-252022-04-2520211864063Xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091243939&doi=10.1002%2fjbio.202000271&partnerID=40&md5=dee08ed0647346761dbc504b49145214https://scholars.lib.ntu.edu.tw/handle/123456789/607104The 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, noninvasive, 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. ? 2020 Wiley-VCH GmbHcomputer-aided diagnosisconvolutional neural networkdeep learningoptical coherence tomographysquamous cell carcinomaDiagnosisLearning algorithmsOptical tomographySignal detectionTomographyClassification accuracyDetection algorithmEpithelial tissueFull-field optical coherence tomographiesSquamous cell carcinomaSub-micron resolutionsThree-dimensional structureTime-consuming procedureDeep learningalgorithmanimaldiagnostic imagingintestine tumormouseAlgorithmsAnimalsCarcinoma, Squamous CellDeep LearningIntestinal NeoplasmsMiceTomography, Optical Coherence[SDGs]SDG3journal article10.1002/jbio.202000271328883822-s2.0-85091243939