A Deep Neural Network for Cervical Cell Classification Based on Cytology Images

Cervical cancer is one of the most common cancers among women. Fortunately, cervical cancer is treatable if it is diagnosed timely and administered appropriately. The death rate of cervical cancer has been greatly reduced since Pap smear test was applied. However, Pap smear test is a time-consuming and error-prone process. Moreover, classifying cervical cells into different categories is clinically meaningful but also challenging in the field of cervical cancer detection. To address these concerns, computer-aided diagnosis systems with deep learning need to be designed to automatically analyze cervical cytology images. In this study, we construct a deep convolutional neural network with feature representations learned via multiple kernels with different sizes to automatically classify cervical cytology images, named DeepCELL. Firstly, we design three different basic modules of DeepCELL to capture feature information via multiple kernels with different sizes. Then, we stack several such basic modules to form the cervical cell classification model. Finally, we perform a series of experiments to evaluate the proposed method on two cervical cytology datasets: Herlev and SIPaKMeD. Our method achieves the accuracy of 95.628%, precision of 95.685%, recall of 95.647% and F-score of 95.636% on SIPaKMeD dataset, which are the highest among all competing methods. Similarly, our method also achieves satisfactory result on Herlev dataset. In summary, extensive experimental results demonstrate that our proposed method has a promising performance in cervical cell image classification.

Automated summary

In this study, a deep learning model named DeepCELL was constructed to automatically classify cervical cytology images using multiple kernel feature representations. The experimental results showed that the proposed method achieved excellent performance on two datasets, indicating its promising performance in cervical cell image classification.