Segmentation of Cytology Images to Detect Cervical Cancer Using Deep Learning Techniques

Cervical cancer is the fourth most common cancer among women. Every year, more than 200,000 women die due to cervical cancer; however, it is a preventable disease if detected early. This study aims to detect cervical cancer by identifying the cytoplasm and nuclei from the background using deep learning techniques to automate the separation of a single cell. To preprocess the image, resizing and enhancement are adopted by adjusting the brightness and contrast of the image to remove noise in the image. The data is divided into 80% for training and 20% for testing to create models using deep neural networks. The U-Net serves as baseline network for image segmentation, with VGG19, ResNet50, MobileNet, EfficientNetB2 and DenseNet121 used as backbone. In cytoplasmic segmentation, EfficientNetB2 achieves a precision of 99.02%, while DenseNet121 reaches an accuracy of 98.59% for a single smear cell. For nuclei segmentation, EfficientNetB2 achieves an accuracy of 99.86%, surpassing ResNet50, which achieves 99.85%. As a result, deep learning-based image segmentation shows promising result in separating the cytoplasm and nuclei from the background to detect cervical cancer. This is helpful for cytotechnicians in diagnosis and decision-making.