Brain Tumor is one of the deadliest disease any human can suffer from. Machine Learning bought a whole new dimension to the field of Disease Detection.
Many a times there has been various errors from doctors while giving reports be it like sometimes.
- The doctor makes a mistake and falsely diagnose a patient.
- The doctor fails to diagnose a patient.
Our objective in this project is to create an image classification model that can predict Brain MRI scans that belong to one of the four classes with a reasonably high accuracy. Our dataset has more than 3000 Brain MRI scans which are categorized in four classes - Glioma Tumor, Meningioma Tumor, Pituitary Tumor and No Tumor.
- Developed a Machine Learning model to help diagnose a person has Brain Tumor using his Brain MRI scan with a reasonable accuracy of over 97%.
- Trained state-of-art CNN model EfficientNet B1 on the MRI scanned images to predict the brain tumor with F1-score 82%. The implementation was done in Keras.
- Later build a production built of the model using Streamlit and hosted it on GitHub.
https://share.streamlit.io/apoorv-17/brain-tumor/app.py
Just Upload your Brain MRI Scan file and It will show you your the Type of Brain Tumor.