Diagnosis of cervical precancerous lesions based on multimodal feature changes

• Published in: Computers in biology and medicine Vol. 130; p. 104209
• Main Authors: Peng, Gengyou, Dong, Hua, Liang, Tong, Li, Ling, Liu, Jun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2021; Elsevier Limited
• Subjects: Accuracy; Acetic acid test; Acids; Algorithms; Artificial intelligence; Automatic diagnosis; Biopsy; Cervical cancer; Cervical screening; Cervix; Childrens health; Classification; Cluster analysis; Clustering; Colposcopy; Colposcopy image; Cross correlation; Data acquisition; Deep learning; Diagnosis; Experiments; Feature extraction; Image classification; Internal Medicine; Lesions; Machine learning; Maternal & child health; Medical diagnosis; Medical imaging; Medical personnel; Medical research; Medical screening; Multimodal feature change; Neural networks; Other; Registration; Vagina; Vector quantization; Deep learning; Colposcopy image; Cervical screening; Automatic diagnosis; Acetic acid test; Multimodal feature change; automatic diagnosis; deep learning; cervical screening; colposcopy image; multimodal feature change
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104209

To realize the automatic diagnosis of cervical intraepithelial neoplasia (CIN) cases by preacetic acid test and postacetic acid test colposcopy images, this paper proposes a method of cervical precancerous lesion diagnosis based on multimodal feature changes. First, the preacetic acid test and postacetic acid test colposcopy images were registered based on cross-correlation and projection transformation, and then the cervical region was extracted by the k-means clustering algorithm. Finally, a deep learning network was used to extract features and classify the preacetic acid test and postacetic acid test cervical images after registration. Finally, the proposed method achieves a classification accuracy of 86.3%, a sensitivity of 84.1%, and a specificity of 89.8% in 60 test cases. Experimental results show that this method can make better use of the multimodal features of colposcopy images and has lower requirements for medical staff in the process of data acquisition. It has certain clinical significance in cervical cancer precancerous lesion screening systems. •A diagnosis method of cervical precancer based on the preacetic and postacetic acid test cervical images was developed.•A deep learning network was used to extract features and classify the registered cervical images.•The proposed method explored different strategies for classification.