AI Guided Early Screening of Cervical Cancer

TL;DR

This paper presents a comprehensive preprocessing pipeline for medical imaging data to improve machine learning models for early cervical cancer screening, emphasizing data quality and augmentation for better anomaly detection.

Contribution

It introduces a scalable, flexible preprocessing framework that enhances image quality and prepares data effectively for machine learning-based cervical cancer screening.

Findings

Enhanced image quality through artifact removal and contrast adjustment.

Effective data augmentation improves model robustness.

Pipeline integrates seamlessly with clinical decision-support systems.

Abstract

In order to support the creation of reliable machine learning models for anomaly detection, this project focuses on preprocessing, enhancing, and organizing a medical imaging dataset. There are two classifications in the dataset: normal and abnormal, along with extra noise fluctuations. In order to improve the photographs' quality, undesirable artifacts, including visible medical equipment at the edges, were eliminated using central cropping. Adjusting the brightness and contrast was one of the additional preprocessing processes. Normalization was then performed to normalize the data. To make classification jobs easier, the dataset was methodically handled by combining several image subsets into two primary categories: normal and pathological. To provide a strong training set that adapts well to real-world situations, sophisticated picture preprocessing techniques were used, such as contrast enhancement and real-time augmentation (including rotations, zooms, and brightness modifications). To guarantee efficient model evaluation, the data was subsequently divided into training and testing subsets. In order to create precise and effective machine learning models for medical anomaly detection, high-quality input data is ensured via this thorough approach. Because of the project pipeline's flexible and scalable design, it can be easily integrated with bigger clinical decision-support systems.