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README.md

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-license: mit
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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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