A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

Abstract

Cervical diseases, specifically cervical cancer (CC), are among the leading causes of death around the globe, imposing a significant challenge to scientists and healthcare providers dealing with cervical disease patients. None of the existing solutions can detect various cervical diseases, which would lead the experts to accurately detect the early stages of cervical diseases due to the equipment limitations and the type of medical detection tests used in those solutions. New technologies have been developed to enable more rapid and sensitive cervical cancer screening using deep learning algorithms. This study proposes a predictive model using deep learning (DL) algorithms and colposcopy images to detect different classes of cervical diseases, including different stages of cervical diseases. This offers the medical sector an opportunity for early-stage diagnosis of cervical diseases. Four rounds of experiments were conducted in this research to evaluate the performance of the proposed model. According to the results, the proposed model can detect classes (stages) of cervical diseases while it obtains high accuracy. The rate of accuracy in the training stage was above 92%, and the highest achieved accuracy was 99% in the third experiment. Also, in this round of the experiment, the model could achieve the highest performance results in accuracy, and sensitivity with values of 98% and 98%, respectively. Notably, the third and last experiments achieved a perfect specificity value of 1.

Keywords

• Deep learning
• classification
• detection model
• cervical cancer
• cervigram analysis
• colposcopy image