Breast Cancer Using MRI Images

Breast cancer is one of the leading causes of death globally, and early detection plays a crucial role in improving survival rates. This project aims to develop a machine learning-based system for classifying breast cancer from MRI images into two categories: Healthy and Sick. The system leverages deep learning models such as Convolutional Neural Networks (CNN), EfficientNet, and MobileNet, which are known for their high accuracy in image classification tasks. The integration of Grad-CAM (Gradient-weighted Class Activation Mapping) provides a mechanism for explaining model predictions, allowing users to visualize which regions of the MRI images contributed to the classification decision.

The dataset used for training the model is sourced from Kaggle and consists of labeled MRI images of breast cancer patients. Preprocessing steps include image resizing, normalization, and data augmentation to enhance model performance. The backend of the system is developed using Flask, which enables the deployment of the model on a web-based interface, while the frontend is designed using HTML, CSS, and JavaScript for an intuitive user experience.

This project demonstrates the application of deep learning for medical image classification and aims to assist healthcare professionals in diagnosing breast cancer more accurately. By leveraging machine learning techniques and model explainability through Grad-CAM, the system provides a transparent and efficient solution for breast cancer detection.

Keywords: Breast cancer, MRI images, machine learning, deep learning, Convolutional Neural Networks, EfficientNet, MobileNet, Grad-CAM, image classification, Flask.