阴道镜检查
宫颈癌
人工智能
模式识别(心理学)
特征(语言学)
计算机科学
分割
特征提取
医学
癌症
内科学
语言学
哲学
作者
Jun Liu,Xiaoxue Sun,Rihui Li,Yuanxiu Peng
出处
期刊:Current Medical Imaging Reviews
[Bentham Science]
日期:2022-11-01
卷期号:18 (11): 1204-1213
被引量:1
标识
DOI:10.2174/1573405618666220428104541
摘要
Cervical cancer is a high incidence of cancer in women and cervical precancerous screening plays an important role in reducing the mortality rate.In this study, we proposed a multichannel feature extraction method based on the probability distribution features of the Acetowhite (AW) region to identify cervical precancerous lesions, with the overarching goal to improve the accuracy of cervical precancerous screening. A k-means clustering algorithm was first used to extract the cervical region images from the original colposcopy images. We then used a deep learning model called DeepLab V3+ to segment the AW region of the cervical image after the acetic acid experiment, from which the probability distribution map of the AW region after segmentation was obtained. This probability distribution map was fed into a neural network classification model for multichannel feature extraction, which resulted in the final classification performance.Results of the experimental evaluation showed that the proposed method achieved an average accuracy of 87.7%, an average sensitivity of 89.3%, and an average specificity of 85.6%. Compared with the methods that did not add segmented probability features, the proposed method increased the average accuracy rate, sensitivity, and specificity by 8.3%, 8%, and 8.4%, respectively.Overall, the proposed method holds great promise for enhancing the screening of cervical precancerous lesions in the clinic by providing the physician with more reliable screening results that might reduce their workload.
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