Multi-feature based benchmark for cervical dysplasia classification evaluation

Pattern Recognition

Volumn 63, Issue , 2017, Pages 468-475

  Xu, Tao ^a   Zhang, Han ^b   Xin, Cheng ^a   Kim, Edward ^c   Long, L Rodney ^d   Xue, Zhiyun ^d   Antani, Sameer ^d   Huang, Xiaolei ^a  

^a Lehigh University   (United States)

^b RUTGERS UNIVERSITY   (United States)

^c VILLANOVA UNIVERSITY   (United States)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Cervical cancer screening; Computer aided diagnosis; Convolutional neural network; Image classification; Local binary patterns; Pyramid histogram

Indexed keywords

BINARY IMAGES; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CONVOLUTION; DIAGNOSIS; DISEASES; GRAPHIC METHODS; NEURAL NETWORKS;

CERVICAL CANCERS; CLASSIFICATION EVALUATION; CONVOLUTIONAL NEURAL NETWORK; DISEASE CLASSIFICATION; LOCAL BINARY PATTERNS; NATIONAL CANCER INSTITUTE; PYRAMID HISTOGRAM; PYRAMID HISTOGRAM OF ORIENTED GRADIENT (PHOG);

IMAGE CLASSIFICATION;

EID: 84995468177     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.09.027     Document Type: Article

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