Diagnosis of cervical precancerous lesions based on multimodal feature changes. (March 2021)

• Record Type: Journal Article
• Authors: Peng, Gengyou; Dong, Hua; Liang, Tong; Li, Ling; Liu, Jun
• Is Part Of: Computers in biology and medicine. Volume 130(2021)
• Publication Date: 2021-03
• Subjects: Acetic acid test -- Colposcopy image -- Automatic diagnosis -- Multimodal feature change -- Cervical screening -- Deep learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104209
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: 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.