Comparisons of pap smear classification with deep learning models

Simple item page
dc.contributor.authorPromworn, Yuttachon
dc.contributor.authorPattanasak, Satjana
dc.contributor.authorPintavirooj, Chuchart
dc.contributor.authorPiyawattanametha, Wibool
dc.date.accessioned2026-08-06T10:24:20Z
dc.date.available2026-08-06T10:24:20Z
dc.date.issued2019-04-01
dc.description.abstractWe presented a comparative work of deep learning models for Pap smear classification. The benchmark parameters used to compare are accuracy, specificity, computation time, and sensitivity. Five convolution neural network models were employed to compare performance in detecting the presence of cervical precancerous or cancerous cells from a Pap smear database. The best deep learning model for multiclass classification is the densenet161 with an efficiency of 68.0% which will use to implement in our custom-made whole slide imager.
dc.identifier.citationProceedings of the 14th Annual IEEE International Conference on Nano Micro Engineered and Molecular Systems NEMS 2019, 282-285, 2019
dc.identifier.doi10.1109/NEMS.2019.8915600
dc.identifier.other2-s2.0-85076681237
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9791
dc.sourceProceedings of the 14th Annual IEEE International Conference on Nano Micro Engineered and Molecular Systems NEMS 2019
dc.subjectCervical cancer
dc.subjectConvolution neural network
dc.subjectDeep learning
dc.subjectPap smear
dc.subjectWSI
dc.titleComparisons of pap smear classification with deep learning models
dc.typeConference Paper

Files

Collections

Research