Paper Title
Early Detection of Breast Cancer Using Pixel Level Balancing and Augmented UNET

Abstract
Breast cancer is a malignant disease that affects a large population of individuals worldwide and is a significant global health concern. However, existing detection methods often encounter challenges in precisely identifying subtle abnormalities in medical images, leading to potential misdiagnosis and delayed treatment initiation. The need thus arises to necessitate accurate detection for timely intervention and improved patient outcomes. In this paper, we present a comprehensive approach to using annotated medical images by carrying out Pixel Level balancing as it enhances the consistency of image features, ensuring more reliability and high accuracy. Neural networks such as UNET are instrumental in breast cancer detection by extracting complex features from medical images. Trained on large datasets, these networks can detect subtle abnormalities and patterns suggestive of breast cancer.By applying Pixel Level Balancing we add an impactful step used to not only stabilize the image components but also contribute to the reduction of false positives and promising false negatives.This approach ensures that patients receive timely treatment and it improves, and ultimately improves healthcare practices and patient outcomes in oncology. Keywords - UNET, Pixel Level Balancing, Machine Learning, Medical Image Analysis, Early Detection, Disease Classification