光学相干层析成像
人工智能
计算机科学
乳腺癌
支持向量机
模式识别(心理学)
医学
医学物理学
癌症
放射科
内科学
作者
Sautami Basu,Ravinder Agarwal,Vishal Srivastava
标识
DOI:10.1002/jbio.202200385
摘要
Biopsy is the gold standard for cancer detection, however the surge in breast cancer cases has made manual haematoxylin and eosin stained histopathological image examination difficult. Automatic cancer diagnosis is vital for a healthy life. It allows fast diagnosis without specific skills. This research proposes an intelligent full-field polarization-sensitive optical coherence tomography (FF-PS-OCT) system for ex-vivo breast classification using ensemble model corroborated by technique for order preference by similarity to ideal solution (TOPSIS). 220 samples image were scanned using the FF-PS-OCT to extract the phase information. The multilevel ensemble classifier has 94.8% precision, 92.5% recall, 93.7% F-score and 82.3% Mathews correlation coefficient on the testing dataset. The developed ensemble model corroborated by TOPSIS, outperforms the single model in terms of performance metrics. The initial results indicate that the rapid, non-contact and label-free FF-PS-OCT imaging modality using birefringent information is beneficial for making interventional decisions by clinicians.
科研通智能强力驱动
Strongly Powered by AbleSci AI