Our project aims to make cancer detection faster and more accurate by providing an accessible prediction of what kind of cancer a patient has based on MRI images. Our system is designed to reduce the cost of report generation by pathologists and replace the complex traditional system with a simple web interface.

We are continuously working to add more disease detection features, such as lung cancer, in the future. Our goal is to provide doctors with an easy-to-use web interface where they can upload MRI or CT scan images and receive patient reports quickly, which will help detect these diseases faster and provide patients with the necessary treatment.

Additionally, we offer a one-on-one video call feature between doctors and patients, a 24/7 chatbot health assistant that provides information related to these disease and a news and article section that keeps doctors up-to-date with the latest research and articles.

Dataset: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset

Model: https://www.kaggle.com/code/samarthsoni106/brain-tumor-detection-using-tensorflow

Note: Dataset contains 7023 images of human brain MRI images which are classified into 4 classes: glioma - meningioma - no tumor and pituitary.

It has an accuracy of 95 percent.