Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression
André Aquilina,
Emmanouil Papagiannakis
Abstract:Objective
A deep learning classifier that improves the accuracy of colposcopic impression.
Methods
Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subset… Show more
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