An Analytical Model to Identify Cervical Conditions using Digital Colposcopy Images and Convolutional Neural Network Algorithms

Abstract

Cervical diseases, including cervical cancer (CC), present a significant global health challenge, emphasizing the critical need for accurate early detection methods. Current solutions face limitations due to equipment constraints and the nature of medical detection tests employed. This paper proposes a predictive model utilizing deep learning algorithms and colposcopy images to detect various classes and stages of cervical diseases. Leveraging Convolutional Neural Networks (CNNs) and diverse models such as EfficientNetB0, VGG16, ResNet50, and more, the study aims to surpass the 90% accuracy achieved by VGG16 in detecting cervical diseases. By exploring alternative techniques like Xception, InceptionV3, the goal is to achieve a superior accuracy rate of 95% or higher. Additionally, the project extends to building a user-friendly front end using the Flask framework, facilitating user testing with authentication. Through these efforts, the research seeks to significantly enhance the effectiveness of cervical disease detection, offering a promising avenue for improving healthcare outcomes and saving lives globally.