| --- |
| license: mit |
| language: |
| - en |
| pipeline_tag: image-classification |
| library_name: keras |
| --- |
| |
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| # 🧠 Brain Tumor Multi-Class Classification (TensorFlow) |
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| ## Model Details |
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| * **Model Name:** Brain Tumor Classification Model |
| * **Model Type:** Multi-class Image Classification |
| * **Framework:** TensorFlow / Keras |
| * **Architecture:** Convolutional Neural Network (CNN) *(or specify: e.g., EfficientNetB0, ResNet50, etc.)* |
| * **Task:** Classify brain MRI images into four categories: |
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| * No Tumor |
| * Meningioma |
| * Glioma |
| * Pituitary Tumor |
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|
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| ## Intended Use |
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| ### Primary Use Cases |
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| * Assistive diagnostic tool for detecting brain tumor types from MRI scans |
| * Educational and research purposes |
| * Prototype for AI-powered medical imaging systems |
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| ### Out-of-Scope Use |
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| * Not intended for real-world clinical diagnosis without expert validation |
| * Should not replace medical professionals |
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| --- |
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| ## Dataset |
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| * **Type:** Brain MRI images |
| * **Classes:** 4 (No Tumor, Meningioma, Glioma, Pituitary) |
| * **Input Shape:** (e.g., 224 × 224 × 3) |
| * **Preprocessing:** |
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| * Resizing to fixed dimensions |
| * Normalization (pixel values scaled to [0,1]) |
| * Data augmentation (rotation, flipping, zooming) |
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| *(Add dataset source if public, e.g., Kaggle or hospital dataset)* |
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| --- |
|
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| ## Model Architecture |
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| * Base model: *(e.g., EfficientNetB0 / Custom CNN)* |
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| * Layers: |
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| * Convolutional layers for feature extraction |
| * Pooling layers |
| * Fully connected dense layers |
| * Softmax output layer (4 neurons) |
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| * **Loss Function:** Categorical Crossentropy |
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| * **Optimizer:** Adam |
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| * **Metrics:** Accuracy, Precision, Recall |
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| --- |
|
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| ## Training Details |
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| * **Epochs:** (e.g., 20–50) |
| * **Batch Size:** (e.g., 16 or 32) |
| * **Train/Validation Split:** (e.g., 80/20) |
| * **Hardware:** CPU / GPU *(specify if available)* |
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| --- |
|
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| ## Evaluation Results |
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| | Metric | Value (example) | |
| | --------- | --------------- | |
| | Accuracy | 92% | |
| | Precision | 91% | |
| | Recall | 90% | |
| | F1 Score | 90% | |
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| ### Confusion Matrix Insights |
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| * Strong performance on **No Tumor** and **Pituitary** |
| * Some confusion between **Glioma** and **Meningioma** (common in MRI tasks) |
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| --- |
|
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| ## Limitations |
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| * Performance depends heavily on dataset quality and diversity |
| * May not generalize well to: |
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| * Different MRI machines |
| * Different populations |
| * Class imbalance can affect predictions |
| * Cannot explain predictions (unless paired with explainability tools like Grad-CAM) |
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| --- |
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| ## Ethical Considerations |
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| * Risk of misclassification in sensitive medical contexts |
| * Must include human oversight in real applications |
| * Dataset bias could affect fairness across demographics |
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| --- |
|
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| ## How to Use |
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|
| ```python |
| import tensorflow as tf |
| import numpy as np |
| import cv2 |
| |
| model = tf.keras.models.load_model("model.h5") |
| |
| img = cv2.imread("image.jpg") |
| img = cv2.resize(img, (224, 224)) |
| img = img / 255.0 |
| img = np.expand_dims(img, axis=0) |
| |
| prediction = model.predict(img) |
| classes = ["No Tumor", "Meningioma", "Glioma", "Pituitary"] |
| |
| print("Prediction:", classes[np.argmax(prediction)]) |
| ``` |
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| --- |
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| ## Model Outputs |
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| * **Input:** MRI brain image |
| * **Output:** Probability distribution across 4 classes |
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| Example: |
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| ``` |
| [0.85, 0.05, 0.07, 0.03] |
| → No Tumor |
| ``` |
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| --- |
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| ## Future Improvements |
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| * Use transfer learning (EfficientNet, Vision Transformers) |
| * Add explainability (Grad-CAM heatmaps) |
| * Deploy with optimized inference (TensorFlow Lite) |
| * Improve dataset diversity and size |
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| --- |
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| ## License |
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| * Specify license (e.g., MIT, Apache 2.0) |
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| --- |
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| ## Contact |
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| * Name: *(Your Name)* |
| * Project: *(e.g., NeuroScopeAI)* |
| * Email/GitHub: *(optional)* |
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| --- |
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| If you want, I can **upgrade this into a Hugging Face model card format (README.md)** so you can upload it directly with your model. |
