README.md

metadata

license: mit

🧠 Multi-Cancer Image Classification

This project aims to classify different types of cancer images using a deep learning Convolutional Neural Network (CNN). The model is built with TensorFlow/Keras.

📂 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

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

Save the model

model.save("image_classifier.h5")

Predict on a new image

gercek_deger("test_image.jpg", model, train_generator.class_indices)

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