  Various screening and diagnostic methods have led to a large reduction of
cervical cancer death rates in developed countries. However, cervical cancer is
the leading cause of cancer related deaths in women in India and other low and
middle income countries (LMICs) especially among the urban poor and slum
dwellers. Several sophisticated techniques such as cytology tests, HPV tests
etc. have been widely used for screening of cervical cancer. These tests are
inherently time consuming. In this paper, we propose a convolutional
autoencoder based framework, having an architecture similar to SegNet which is
trained in an adversarial fashion for classifying images of the cervix acquired
using a colposcope. We validate performance on the Intel-Mobile ODT cervical
image classification dataset. The proposed method outperforms the standard
technique of fine-tuning convolutional neural networks pre-trained on ImageNet
database with an average accuracy of 73.75%.
