metadata
license: mit
datasets:
- masoudnickparvar/brain-tumor-mri-dataset
metrics:
- accuracy
pipeline_tag: image-classification
library_name: keras
tags:
- cnn
- keras
- brain-tumor
- medical-imaging
- tensor-flow
- image-classification
language:
- en
Brain Tumor Detection CNN Model
This model was trained using a Convolutional Neural Network (CNN) to classify brain MRI images as either having a tumor or not. It uses Keras with TensorFlow backend and was trained on the publicly available Brain Tumor MRI Dataset from Kaggle. Dataset
The dataset contains 3,762 T1-weighted contrast-enhanced MRI images, labeled as:
- Yes – Images with a brain tumor
- No – Images without a brain tumor
The data is balanced and preprocessed into two folders: yes/ and no/.
Train Accuracy: ~98% Validation Accuracy: ~96%
🧠 Model Architecture
- Type: CNN
- Framework: Keras (TensorFlow backend)
- Input shape:
(150, 150, 3) - Final Activation:
sigmoid - Loss:
binary_crossentropy - Optimizer:
Adam
Example (simplified):
model = Sequential([
Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
MaxPooling2D(2,2),
Conv2D(64, (3,3), activation='relu'),
MaxPooling2D(2,2),
Flatten(),
Dense(128, activation='relu'),
Dense(1, activation='sigmoid')
])