2020/04/28 by Abhilash Nandy, Nandy, Abhilash, Rachana Sathish +3
Computer Science · Engineering · Medicine · #AI in cancer detection #Cervical Cancer and HPV Research #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.13406
9 pages, 10 images, 5th MedImage Workshop of 11th Indian Conference on Computer Vision, Graphics and Image Processing, Hyderabad, India, 2018
arxiv created 2020/04/28 · openalex publication_date 2020/04/28 · arxiv updated 2020/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.