Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images.Cervical cancer is the most common cancer among women worldwide.  The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN) that identifies cervical malignancies and provides diagnostic interpretability. 
Convolutional Neural Networks
AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. 
AlexNet
AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. 
VGG-19
AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images.  The Xception model had excellent performance in identifying cervical SCC and AC in test sets. 
Xception
AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. 
ResNet-50