--- a
+++ b/Lung Cancer Prediction.py
@@ -0,0 +1,193 @@
+# Mount Google Drive to access dataset files
+from google.colab import drive
+drive.mount('/content/drive', force_remount=True)
+
+# Define paths to the training, validation, and test datasets
+train_folder = '/content/drive/MyDrive/dataset/train'
+test_folder = '/content/drive/MyDrive/dataset/test'
+validate_folder = '/content/drive/MyDrive/datasetvalid'
+
+# Define paths to the specific classes within the dataset
+normal_folder = '/normal'
+adenocarcinoma_folder = '/adenocarcinoma_left.lower.lobe_T2_N0_M0_Ib'
+large_cell_carcinoma_folder = '/large.cell.carcinoma_left.hilum_T2_N2_M0_IIIa'
+squamous_cell_carcinoma_folder = '/squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa'
+
+# Import necessary libraries
+import warnings
+warnings.filterwarnings('ignore')
+
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import LabelEncoder
+
+import tensorflow as tf
+import tensorflow.keras
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, SpatialDropout2D, Activation, Lambda, Flatten, LSTM
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
+from tensorflow.keras.optimizers import Adam, RMSprop
+from tensorflow.keras import utils
+
+print("Libraries Imported")
+
+# Set the image size for resizing
+IMAGE_SIZE = (350, 350)
+
+# Initialize the image data generators for training and testing
+train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True)
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+# Define the batch size for training
+batch_size = 8
+
+# Create the training data generator
+train_generator = train_datagen.flow_from_directory(
+    train_folder,
+    target_size=IMAGE_SIZE,
+    batch_size=batch_size,
+    color_mode="rgb",
+    class_mode='categorical'
+)
+
+# Create the validation data generator
+validation_generator = test_datagen.flow_from_directory(
+    test_folder,
+    target_size=IMAGE_SIZE,
+    batch_size=batch_size,
+    color_mode="rgb",
+    class_mode='categorical'
+)
+
+# Set up callbacks for learning rate reduction, early stopping, and model checkpointing
+from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint
+
+learning_rate_reduction = ReduceLROnPlateau(monitor='loss', patience=5, verbose=2, factor=0.5, min_lr=0.000001)
+early_stops = EarlyStopping(monitor='loss', min_delta=0, patience=6, verbose=2, mode='auto')
+checkpointer = ModelCheckpoint(filepath='best_model.hdf5', verbose=2, save_best_only=True, save_weights_only=True)
+
+# Define the number of output classes
+OUTPUT_SIZE = 4
+
+# Load a pre-trained model (Xception) without the top layers and freeze its weights
+pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
+pretrained_model.trainable = False
+
+# Create a new model with the pre-trained base and additional layers for classification
+model = Sequential()
+model.add(pretrained_model)
+model.add(GlobalAveragePooling2D())
+model.add(Dense(OUTPUT_SIZE, activation='softmax'))
+
+print("Pretrained model used:")
+pretrained_model.summary()
+
+print("Final model created:")
+model.summary()
+
+# Compile the model with an optimizer, loss function, and evaluation metric
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Train the model with the training and validation data generators
+history = model.fit(
+    train_generator,
+    steps_per_epoch=25,
+    epochs=50,
+    callbacks=[learning_rate_reduction, early_stops, checkpointer],
+    validation_data=validation_generator,
+    validation_steps=20
+)
+
+print("Final training accuracy =", history.history['accuracy'][-1])
+print("Final testing accuracy =", history.history['val_accuracy'][-1])
+
+# Function to display training curves for loss and accuracy
+def display_training_curves(training, validation, title, subplot):
+    if subplot % 10 == 1:
+        plt.subplots(figsize=(10, 10), facecolor='#F0F0F0')
+        plt.tight_layout()
+    ax = plt.subplot(subplot)
+    ax.set_facecolor('#F8F8F8')
+    ax.plot(training)
+    ax.plot(validation)
+    ax.set_title('model ' + title)
+    ax.set_ylabel(title)
+    ax.set_xlabel('epoch')
+    ax.legend(['train', 'valid.'])
+
+# Display training curves for loss and accuracy
+display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 211)
+display_training_curves(history.history['accuracy'], history.history['val_accuracy'], 'accuracy', 212)
+
+# Save the trained model
+model.save('/content/drive/MyDrive/dataset/trained_lung_cancer_model.h5')
+
+# Function to load and preprocess an image for prediction
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+def load_and_preprocess_image(img_path, target_size):
+    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
+
+# Load, preprocess, and predict the class of an image
+img_path = '/content/sq.png'
+img = load_and_preprocess_image(img_path, IMAGE_SIZE)
+predictions = model.predict(img)
+predicted_class = np.argmax(predictions[0])
+class_labels = list(train_generator.class_indices.keys())
+predicted_label = class_labels[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=IMAGE_SIZE))
+plt.title(f"Predicted: {predicted_label}")
+plt.axis('off')
+plt.show()
+
+# Repeat the process for additional images
+img_path = '/content/ad3.png'
+img = load_and_preprocess_image(img_path, IMAGE_SIZE)
+predictions = model.predict(img)
+predicted_class = np.argmax(predictions[0])
+predicted_label = class_labels[predicted_class]
+print(f"The image belongs to class: {predicted_label}")
+plt.imshow(image.load_img(img_path, target_size=IMAGE_SIZE))
+plt.title(f"Predicted: {predicted_label}")
+plt.axis('off')
+plt.show()
+
+img_path = '/content/l3.png'
+img = load_and_preprocess_image(img_path, IMAGE_SIZE)
+predictions = model.predict(img)
+predicted_class = np.argmax(predictions[0])
+predicted_label = class_labels[predicted_class]
+print(f"The image belongs to class: {predicted_label}")
+plt.imshow(image.load_img(img_path, target_size=IMAGE_SIZE))
+plt.title(f"Predicted: {predicted_label}")
+plt.axis('off')
+plt.show()
+
+img_path = '/content/n8.jpg'
+img = load_and_preprocess_image(img_path, IMAGE_SIZE)
+predictions = model.predict(img)
+predicted_class = np.argmax(predictions[0])
+predicted_label = class_labels[predicted_class]
+print(f"The image belongs to class: {predicted_label}")
+plt.imshow(image.load_img(img_path, target_size=IMAGE_SIZE))
+plt.title(f"Predicted: {predicted_label}")
+plt.axis('off')
+plt.show()