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Author: search.filters.author.Promworn, YuttachonSubject: search.filters.subject.Cervical cancer

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    Comparisons of pap smear classification with deep learning models
    (2019-04-01)
    Promworn, Yuttachon
    ;
    Pattanasak, Satjana
    ;
    Pintavirooj, Chuchart
    ;
    Piyawattanametha, Wibool
    We 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.
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    A comparative study of 3 deep learning models for Pap smear screening
    (2019-01-10)
    Promworn, Yuttachon
    ;
    Pintavirooj, C.
    ;
    Piyawattanametha, Wibool
    This work presents a comparative study of automated screening procedure for Pap smear with deep learning technology. Three convolution neural network models (AlexNet, densenet161 and resnet101) were employed for detecting the presence of cervical precancerous or cancerous cells from Pap smear database. The study compares accuracy, sensitivity, specificity, and computation time for each deep learning model. The best model is the densenet161 due to its high sensitivity and accuracy which are key factors in an automated Pap smear screening procedure to offer the best early detection of cervical cancer to better treatment outcomes.