🧠 Multi-Cancer Image Classification

📂 Project Structure

• Data preprocessing is done with ImageDataGenerator (normalization and train/validation split).
• The CNN model is created with Conv2D, MaxPooling2D, and Dense layers.
• After training, the model is saved as .h5.
• The function gercek_deger() allows testing predictions on a single image.

⚙️ Dependencies

• TensorFlow / Keras
• NumPy
• Matplotlib

🚀 Model Architecture

1. Conv2D + MaxPooling2D → Feature extraction from images
2. Conv2D + MaxPooling2D
3. Conv2D + MaxPooling2D
4. Flatten → Convert data into a vector
5. Dense (512, ReLU) → Fully connected layer
6. Dense (Softmax) → Output layer with class probabilities

🏋️ Training

• Input image size: 150x150
• Batch size: 32
• Optimizer: Adam
• Loss: Categorical Crossentropy
• Metrics: Accuracy
• Epochs: 10

📊 Usage

Train the model

Save the model

Predict on a new image