CANet-v1.1

📂 Project Structure

.
├── image_classifier.h5     # Trained model
├── main.py                 # Training & prediction script
└── README.md               # Project description

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⚙️ Technologies Used

• Python 3.10+
• TensorFlow / Keras
• NumPy
• Matplotlib

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📊 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.

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🏗️ 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

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🚀 Training

model.fit(train_generator, validation_data=validation_generator, epochs=10)

After training, the model is saved as:

model.save("image_classifier.h5")

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🔮 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()

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✅ 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