A comparative study of 3 deep learning models for Pap smear screening

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.