BREAST CANCER DIAGNOSIS

Abstract

Breast cancer is one of the leading causes of death among women worldwide, and early detection plays a crucial role in improving survival rates. This project focuses on the development of an intelligent system for breast cancer diagnosis using advanced deep learning techniques using MobileNetV2. The proposed system analyzes breast cancer histopathology images, to classify whether a tumor is benign or malignant. By utilizing algorithms such as neural networks and other classification models, the system provides accurate and efficient predictions, thereby reducing the chances of human error in diagnosis. The model is trained on relevant datasets to learn patterns and improve its performance over time. In addition to prediction, the system offers a user-friendly interface that allows healthcare professionals to input patient data and receive instant diagnostic results. This solution aims to assist doctors in making faster and more reliable decisions, ultimately contributing to early detection and better treatment outcomes. Overall, this project enhances diagnostic accuracy, reduces manual effort, and provides a cost-effective and efficient tool for breast cancer detection, making it highly valuable in modern healthcare systems.