Recognition of Cervical Precancerous Lesions Based on Probability Distribution
Feature Guidance
Introduction:
Cervical cancer is a high incidence of cancer in women and cervical precancerous
screening plays an important role in reducing the mortality rate.
Methods:
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:
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.
Conclusion:
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.
