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license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
---
## 📂 Project Structure
```
.
├── image_classifier.h5 # Trained model
├── main.py # Training & prediction script
└── README.md # Project description
```
---
## ⚙️ Technologies Used
* Python 3.10+
* TensorFlow / Keras
* NumPy
* Matplotlib
---
## 📊 Dataset
The dataset is from **Kaggle Multi-Cancer Dataset**:
```
/kaggle/input/multi-cancer/Multi Cancer/Multi Cancer/Breast Cancer
```
Images are split into **90% training** and **10% validation** using `ImageDataGenerator`.
---
## 🏗️ Model Architecture
* **Conv2D (32 filters, 3x3, ReLU)**
* **MaxPooling2D (2x2)**
* **Conv2D (64 filters, 3x3, ReLU)**
* **MaxPooling2D (2x2)**
* **Conv2D (128 filters, 3x3, ReLU)**
* **MaxPooling2D (2x2)**
* **Flatten**
* **Dense (512, ReLU)**
* **Dense (Softmax output, # of classes)**
Optimizer: **Adam**
Loss: **Categorical Crossentropy**
Metric: **Accuracy**
---
## 🚀 Training
```python
model.fit(train_generator, validation_data=validation_generator, epochs=10)
```
After training, the model is saved as:
```python
model.save("image_classifier.h5")
```
---
## 🔮 Prediction Example
```python
def guess(image_path, model, class_indices):
img = load_img(image_path, target_size=(150, 150))
img_array = img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
prediction = model.predict(img_array)
predicted_class = np.argmax(prediction)
class_labels = {v: k for k, v in class_indices.items()}
predicted_label = class_labels[predicted_class]
plt.imshow(img)
plt.title(f"Model guess: {predicted_label}")
plt.axis("off")
plt.show()
```
---
## ✅ Results
* Trains a CNN model for breast cancer image classification
* Provides a simple **guess() function** to visualize predictions
* Model is reusable via `image_classifier.h5` |