Multi-feature based benchmark for cervical dysplasia classification evaluation

详细信息    查看全文

• 作者：Tao Xu^aAuthor Vitae ; Han Zhang^bAuthor Vitae ; Cheng Xin^aAuthor Vitae ; Edward Kim^cAuthor Vitae ; L. Rodney Long^dAuthor Vitae ; Zhiyun Xue^dAuthor Vitae ; Sameer Antani^dAuthor Vitae ; Xiaolei Huang^a ; ^{xih206@lehigh.edu}Author Vitae
• 关键词：Cervical cancer screening ; Computer aided diagnosis ; Image classification ; Pyramid histogram ; Local binary patterns ; Convolutional neural network
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：468-475
• 全文大小：921 K
• 卷排序：63

文摘

A new image dataset is introduced for evaluating cervical disease classification. The pyramid feature descriptor encodes complementary hand-crafted features. The deep convolutional neural network automatically learns features from images. Results using seven classic classifiers serve as the baseline for future research.