Cervical Cancer Prediction Using Image Dataset

Abstract

Cervical cancer is a leading cancer in the female
population. This disease is considered dangerous as
its slow and unpredicted growth. The prevention of
such cancer can be mostly achieved by screening
its transformation zones. The cervical precancerous
zones can be considered as three types:
type 1, type 2, and type 3. Screening and analyzing
these three stages can be crucial for preventing
their transformation into cancer. Hence, it is
essentially important to have an automated and
intelligent system that can grade the cervical precancerous
colposcopy images into one of the three
types. This can help in providing the right
treatment and prevent cancer transformation. In this
paper, we develop a residual learning-based model
(ResNet-50) to be trained for classifying the type of
a colposcopy cervical image into type 1, type 2,
and type 3. Experimentally, the model was finetuned
and evaluated on a public dataset of
colposcopy cervical images and achieved
promising results in cervical cancer screening of
accuracy of 77% and F1-score of 79%.