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 pre-cancerous 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 fine-tuned 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%.