  Fine-grained classification of cervical cells into different abnormality
levels is of great clinical importance but remains very challenging. Contrary
to traditional classification methods that rely on hand-crafted or engineered
features, convolution neural network (CNN) can classify cervical cells based on
automatically learned deep features. However, CNN in previous studies do not
involve cell morphological information, and it is unknown whether morphological
features can be directly modeled by CNN to classify cervical cells. This paper
presents a CNN-based method that combines cell image appearance with cell
morphology for classification of cervical cells in Pap smear. The training
cervical cell dataset consists of adaptively re-sampled image patches coarsely
centered on the nuclei. Several CNN models (AlexNet, GoogleNet, ResNet and
DenseNet) pre-trained on ImageNet dataset were fine-tuned on the cervical
dataset for comparison. The proposed method is evaluated on the Herlev cervical
dataset by five-fold cross-validation at patient level splitting. Results show
that by adding cytoplasm and nucleus masks as raw morphological information
into appearance-based CNN learning, higher classification accuracies can be
achieved in general. Among the four CNN models, GoogleNet fed with both
morphological and appearance information obtains the highest classification
accuracies of 94.5% for 2-class classification task and 64.5% for 7-class
classification task. Our method demonstrates that combining cervical cell
morphology with appearance information can provide improved classification
performance, which is clinically important for early diagnosis of cervical
dysplastic changes.
