ABSTRACT
Breast cancer remains one of the most significant health challenges among women across the globe, accounting for a large percentage of cancer-related deaths. Early diagnosis and accurate stage identification play a vital role in determining appropriate treatment and improving survival rates. Traditional methods of detection often rely on manual image inspection, biopsy results, and clinical tests, which may be time-consuming, prone to human error, and dependent on expert interpretation. To overcome these limitations, advancements in machine learning and deep learning have opened new avenues for automated, accurate, and efficient diagnosis systems. This project proposes a comprehensive Breast Cancer Prediction System that integrates multiple intelligent models to assist in early detection and stage classification. The system leverages the power of Convolutional Neural Networks (CNN) for image-based diagnosis and Random Forest Classifier for data-based analysis using clinical features. The CNN model is trained on breast cancer image datasets to predict whether a given image represents a normal or malignant case, while a specialized stage prediction CNN model is developed to classify the cancer into stages such as Stage 1, Stage 2, or Stage 3. Alongside image-based predictions, the project also includes a machine learning model trained on tabular clinical data, where parameters such as age, menopause status, tumor size, invasive nodes, breast quadrant, and metastasis are processed to predict the cancer condition with high accuracy. To provide accessibility and ease of use, the system is deployed through a Flask-based web application, which allows users to interact with the models via a simple and intuitive interface. Users can register and log in securely using a SQLite3 database, upload breast cancer images for CNN-based predictions, and input clinical data for Random Forest predictions. The web interface also displays prediction results along with confidence scores, helping users interpret outcomes effectively.