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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
```python
model.fit(train_generator, validation_data=validation_generator, epochs=10)
```
### Save the model
```python
model.save("image_classifier.h5")
```
### Predict on a new image
```python
gercek_deger("test_image.jpg", model, train_generator.class_indices)
```
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