Breast Cancer Detection Using Mammography Image Processing to Deep Learning with YOLOV8n & YOLOV11n.

This project focuses on the development of an automated system for breast cancer detection using mammography image processing and deep learning techniques. The system employs the YOLOv8n, YOLO11n algorithm, a state-of-the-art object detection model, to analyze mammography images and classify them as either "cancer" or "normal." The goal is to enhance the accuracy and speed of breast cancer diagnosis, reducing the dependency on manual interpretation and providing more reliable results for early-stage detection. The project integrates a web-based user interface, where users can register, log in, and upload their mammography images for processing. Once an image is uploaded, the model processes it and provides a classification result, helping users to identify potential signs of cancer. The backend of the system is developed using Flask, while SQLite is used for securely storing user data and ensuring smooth system operations. This solution aims to make breast cancer detection more accessible and efficient, offering a tool to assist medical professionals in their diagnostic processes. The system is designed with simplicity and user-friendliness in mind, ensuring that users with minimal technical knowledge can effectively use the platform for image classification. The ultimate aim of the project is to assist in early cancer detection, thereby contributing to improved healthcare outcomes.

Keywords: Breast cancer, Mammography, YOLOv8n, YOLO11n, Deep learning, Image classification, Flask, SQLite, Early detection, User interface, Medical diagnostics.