📂 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
model.fit(train_generator, validation_data=validation_generator, epochs=10)
After training, the model is saved as:
model.save("image_classifier.h5")
🔮 Prediction Example
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