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+# Lung Cancer Prediction using CNN and Transfer Learning
+
+This project aims to build a Lung Cancer Prediction System using Convolutional Neural Networks (CNN) and transfer learning. The model classifies lung cancer images into four categories: Normal, Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma.
+
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Dataset](#dataset)
+- [Dependencies](#dependencies)
+- [Project Structure](#project-structure)
+- [Training the Model](#training-the-model)
+- [Using the Model](#using-the-model)
+- [Results](#results)
+- [Acknowledgements](#acknowledgements)
+- [License](#license)
+
+## Introduction
+
+Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection and accurate classification are crucial for effective treatment and patient survival. This project leverages deep learning techniques to develop a robust lung cancer classification model using chest X-ray images.
+
+## Dataset
+
+The dataset used in this project consists of lung cancer images categorized into four classes:
+1. Normal
+2. Adenocarcinoma
+3. Large Cell Carcinoma
+4. Squamous Cell Carcinoma
+
+The dataset should be organized into training (`train`), validation (`valid`), and testing (`test`) folders with the following subfolders for each class:
+
+- `train/`
+  - `normal/`
+  - `adenocarcinoma/`
+  - `large_cell_carcinoma/`
+  - `squamous_cell_carcinoma/`
+
+- `valid/`
+  - `normal/`
+  - `adenocarcinoma/`
+  - `large_cell_carcinoma/`
+  - `squamous_cell_carcinoma/`
+
+- `test/`
+  - `normal/`
+  - `adenocarcinoma/`
+  - `large_cell_carcinoma/`
+  - `squamous_cell_carcinoma/`
+
+Alternatively, you can also download a similar dataset from [Kaggle](https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images) which includes Chest CT scan images.
+
+### Google Colab Link
+To replicate and run the project in Google Colab, use the following link: [Lung Cancer Prediction System on Colab](https://colab.research.google.com/drive/1kMTghEwVoJaFmlKydxuhhoyzHluIUjoV?usp=sharing)
+
+
+### Usage
+
+- **Direct Download**: You can download the dataset directly from this repository and store it on your local system.
+- **Google Drive**: Alternatively, you can store the dataset in your Google Drive and mount it using the provided code to replicate the environment used in this project.
+
+## Dependencies
+
+The project requires the following libraries:
+- Python 3.x
+- pandas
+- numpy
+- seaborn
+- matplotlib
+- scikit-learn
+- tensorflow
+- keras
+
+You can install the required libraries using the following command:
+
+```bash
+pip install pandas numpy seaborn matplotlib scikit-learn tensorflow keras
+```
+
+
+## Project Structure
+
+```
+.
+├── Lung_Cancer_Prediction.ipynb
+├── README.md
+├── dataset
+│ ├── train
+│ │ ├── adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib
+│ │ ├── large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa
+│ │ ├── normal
+│ │ └── squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa
+│ ├── test
+│ │ ├── adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib
+│ │ ├── large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa
+│ │ ├── normal
+│ │ └── squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa
+│ └── valid
+│ ├── adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib
+│ ├── large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa
+│ ├── normal
+│ └── squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa
+└── best_model.hdf5
+```
+
+This structure outlines the files and directories included in your project:
+
+- **Lung_Cancer_Prediction.ipynb**: Jupyter Notebook containing the code for training and evaluating the lung cancer prediction model.
+- **README.md**: Markdown file providing an overview of the project, usage instructions, and other relevant information.
+- **dataset/**: Directory containing the dataset used for training and testing.
+  - **train/**: Subdirectory containing training images categorized into different classes of lung cancer.
+  - **test/**: Subdirectory containing testing images categorized similarly to the training set.
+  - **valid/**: Subdirectory containing validation images categorized similarly to the training set.
+- **best_model.hdf5**: File where the best-trained model weights are saved after training.
+
+
+
+
+## Training the Model
+
+The Jupyter Notebook `Lung_Cancer_Prediction.ipynb` contains the code for training the model. Below are the steps involved:
+
+1. **Mount Google Drive**: To access the dataset stored in Google Drive.
+2. **Load and Preprocess Data**: Use `ImageDataGenerator` for data augmentation and normalization.
+3. **Define the Model**: Use the Xception model pre-trained on ImageNet as the base model and add custom layers on top.
+4. **Compile the Model**: Specify the optimizer, loss function, and metrics.
+5. **Train the Model**: Fit the model on the training data and validate it on the validation data. Callbacks like learning rate reduction, early stopping, and model checkpointing are used.
+6. **Save the Model**: Save the trained model for future use.
+
+### Example Usage
+
+```python
+# Mount Google Drive
+from google.colab import drive
+drive.mount('/content/drive', force_remount=True)
+
+# Load and preprocess data
+IMAGE_SIZE = (350, 350)
+train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True)
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_folder,
+    target_size=IMAGE_SIZE,
+    batch_size=8,
+    class_mode='categorical'
+)
+
+validation_generator = test_datagen.flow_from_directory(
+    validate_folder,
+    target_size=IMAGE_SIZE,
+    batch_size=8,
+    class_mode='categorical'
+)
+
+# Define the model
+pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
+pretrained_model.trainable = False
+
+model = Sequential([
+    pretrained_model,
+    GlobalAveragePooling2D(),
+    Dense(4, activation='softmax')
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model
+history = model.fit(
+    train_generator,
+    steps_per_epoch=25,
+    epochs=50,
+    validation_data=validation_generator,
+    validation_steps=20
+)
+
+# Save the model
+model.save('/content/drive/MyDrive/dataset/trained_lung_cancer_model.h5')
+```
+
+
+
+## Using the Model
+
+To use the trained model for predictions, follow these steps:
+
+1. **Load the Trained Model**: Load the saved `.h5` model file using TensorFlow/Keras.
+2. **Preprocess the Input Image**: Load and preprocess the input image using `image.load_img()` and `image.img_to_array()`.
+3. **Make Predictions**: Use the loaded model to predict the class of the input image.
+4. **Display Results**: Display the input image along with the predicted class label.
+
+### Example Code
+
+```python
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the trained model
+model = load_model('/content/drive/MyDrive/dataset/trained_lung_cancer_model.h5')
+
+def load_and_preprocess_image(img_path, target_size):
+    # Load and preprocess the image
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0  # Rescale the image like the training images
+    return img_array
+
+# Example usage with an image path
+img_path = '/content/test_image.png'
+target_size = (350, 350)
+
+# Load and preprocess the image
+img = load_and_preprocess_image(img_path, target_size)
+
+# Make predictions
+predictions = model.predict(img)
+predicted_class = np.argmax(predictions[0])
+
+# Map the predicted class to the class label
+class_labels = list(train_generator.class_indices.keys())  # Assuming `train_generator` is defined
+predicted_label = class_labels[predicted_class]
+
+# Print the predicted class
+print(f"The image belongs to class: {predicted_label}")
+
+# Display the image with the predicted class
+plt.imshow(image.load_img(img_path, target_size=target_size))
+plt.title(f"Predicted: {predicted_label}")
+plt.axis('off')
+plt.show()
+```
+
+
+
+## Results
+
+After training and evaluating the lung cancer prediction model, the following results were obtained:
+
+- Final training accuracy: `history.history['accuracy'][-1]`
+- Final validation accuracy: `history.history['val_accuracy'][-1]`
+- Model accuracy: 93%
+
+
+### Example Predictions
+
+Include images and their predicted classes here, demonstrating the model's performance on new data.
+
+
+
+
+## Acknowledgements
+
+We acknowledge and thank the contributors to the [Chest CT Scan Images Dataset](https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images) on Kaggle for providing the dataset used in this project.
+
+
+
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).
+
+Feel free to use, modify, or distribute this code for educational and non-commercial purposes. Refer to the LICENSE file for more details.
+
+
+
+
+