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
Code to Run:
import streamlit as st
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
# Load the trained model
@st.cache_resource
def load_model():
return tf.keras.models.load_model('models/brain_tumor_model.h5') # Update path if needed
model = load_model()
# Define class labels
class_names = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
# UI
st.title("🧠 Brain Tumor Detection from MRI")
st.write("Upload an MRI image to detect the type of brain tumor.")
# Upload image
uploaded_file = st.file_uploader("Choose an MRI image", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Show image
img = Image.open(uploaded_file)
st.image(img, caption="🖼️ Uploaded Image", use_container_width=True)
# Preprocessing
img = img.resize((224, 224)) # ✅ Make sure it matches your model's input size
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0
# Prediction
predictions = model.predict(img_array)
confidence = float(np.max(predictions)) * 100
predicted_class = class_names[np.argmax(predictions)]
# Output
st.success(f"🎯 Predicted Tumor Type: **{predicted_class}**")
st.info(f"📊 Model Confidence: **{confidence:.2f}%**")