File size: 2,445 Bytes
0d89ca2 12a087d 0d89ca2 554025f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
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](https://www.kaggle.com/datasets/masoudnickparvar/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:
```python
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}%**")
|