Feature Extraction and Classification of Overlapping
Cervical Cancer Cells using Convolutional Neural
Networks
Journal:
GRENZE International Journal of Computer Theory and Engineering
Authors:
Seema Singh, Aaron Joseph, Elsa Mathew, A Veena, Deepak Yadav
Volume:
3
Issue:
4
Grenze ID:
01.GIJCTE.3.4.18
Pages:
118-122
Abstract
Cervical Cancer is the fourth most common cause of cancer and death in women
[1]. India has one of the largest numbers of cases of cervical cancer and efforts are made to
reduce the number of fatalities by improving the techniques of detection. The steps that are
followed includes obtaining the microscopic images by Liquid Based Cytology(LBC)
method, followed by the classification of the visual input. For this method of classification
convolutional neural network is used which is a very powerful technology for the
classification of the visual input arising from the detection process. The convolutional neural
network used here is the Inception v3. The end-result is the accurate classification of
overlapping as well as non-overlapping cervical cancer cells.