A deep learning-based method for cervical transformation zone classification in colposcopy images

Abstract

BACKGROUND: Colposcopy is one of the common methods of cervical cancer screening. The type of cervical transformation zone is considered one of the important factors for grading colposcopic findings and choosing treatment. OBJECTIVE: This study aims to develop a deep learning-based method for automatic classification of cervical transformation zone from colposcopy images. METHODS: We proposed a multiscale feature fusion classification network to classify cervical transformation zone, which can extract features from images and fuse them at multiple scales. Cervical regions were first detected from original colposcopy images and then fed into our multiscale feature fusion classification network. RESULTS: The results on the test dataset showed that, compared with the state-of-the-art image classification models, the proposed classification network had the highest classification accuracy, reaching 88.49%, and the sensitivity to type 1, type 2 and type 3 were 90.12%, 85.95% and 89.45%, respectively, higher than the comparison methods. CONCLUSIONS: The proposed method can automatically classify cervical transformation zone in colposcopy images, and can be used as an auxiliary tool in cervical cancer screening.

TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

“…The outcome of the survey ascertains that deep learning CAD solutions are a bridge to developing automatic screening of cervical cancer. Colposcopy examination is a pivotal tool for cervical cancer screening that offers a greater degree of accuracy than the human papillomavirus (HPV) and Thin-Prep cytologic test (TCT) tests [15]. During the colposcopy examination, a 5% acetic acid solution is topically administered to the cervical region to accentuate cancerous characteristics [16].…”

“…Yan et al [29] designed a BFCNN, a bilinear fuse convolutional neural network for the segmentation and classification of cervigrams. Yuzhen Cao [15] developed a multiscale feature fusion classification network to classify cervical transformation zone and reported an accuracy of 88.49% with 90.12% sensitivity. Asiedu et al [30] used machine learning methods of using boundary boxes to extract ROI and classify the region through support vector machines.…”

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