Advanced Brain Disorder Detection AI

Brain disorder detection using Deep Learning
Uses MRI brain scan images
Detects multiple brain diseases automatically
Early disease detection and diagnosis
Multi-class classification system
Diseases detected:
Glioma tumor
Pituitary tumor
Meningioma tumor
Alzheimer’s disease
Healthy brain
Uses Convolutional Neural Networks (CNN)
Uses Transfer Learning techniques
Models used:
CNN
VGG16
ResNet50
Dataset collected from Kaggle MRI datasets
Combined brain tumor and Alzheimer datasets
1000 images used for training
250 images used for testing
Image preprocessing and normalization
Pixel values converted from 0–255 to 0–1
Feature extraction using convolution layers
Pooling layers reduce computation
Fully connected layers perform classification
Softmax activation used for multi-class prediction
Hyperparameter tuning performed
Parameters tuned:
Learning rate
Epochs
Batch size
Filter size
Number of filters
Best performance achieved with low learning rate and more epochs
Accuracy achieved above 90%
Performance metrics used:
Accuracy
Precision
Recall
F1-score
Confusion matrix
Technologies used:
Python
TensorFlow
Keras
OpenCV
NumPy
Pandas
Matplotlib
Scikit-learn
Platform used:
Google Colab
Anaconda
Model deployment using Hugging Face and Streamlit
Upload MRI image to predict disease
Fast and automated diagnosis system
Reduces manual work for doctors
Helps in early treatment planning
Non-invasive medical diagnosis method
Future scope:
Add more diseases
Improve accuracy
Use larger datasets
Clinical healthcare implementation