Abstract:
Cervical cancer ranks as the fourth most prevalent cancer worldwide, affecting mostly
the developing countries. However, early diagnosis can help facilitate the clinical
management of the patient. The problem lies in the sparse presence of qualified and
professional health cytotechnicians as compared to the number of people that need to
be diagnosed. Computer-Aided Diagnostic system can be of a lot of help in making
the diagnosis more accurate, reliable, faster and cheaper. Most of the existing
algorithms require precise image segmentation to distinguish the cell. The traditional
machine learning diagnostic system work similarly to the cytopathologists who rely on
handcrafted morphological features such as nucleus area, nucleus-cytoplasm perimeter
ratio, etc to determine the malignancy in a cell. However, our study uses
Convolutional Neural Networks(CNN) which could potentially allow us to eliminate
the computationally expensive tasks of segmentation and feature selection.
Our
results evidenced best accuracy scores of 96.25% for binary classification and
66.87% for seven class classification, which are comparable to the results achieved
with established Machine Learning techniques. This study addresses the different
aspects of training Deep networks on a publicly available cervical cancer database by
Herlev Hospital. We also did a comparative investigation to establish the most suitable
working hyperparameters, optimizers and classifiers for the dataset.
