Identification of Cervical Pathology using Adversarial Neural Networks
This addresses cervical cancer diagnosis in low-resource settings, offering a faster alternative to time-consuming traditional tests, though it is incremental in method.
The paper tackled cervical cancer screening by developing an adversarial neural network framework to classify cervical images, achieving an average accuracy of 73.75% and outperforming standard fine-tuned CNNs.
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%.