--- a
+++ b/training-models/cnn_lstm_model.py
@@ -0,0 +1,71 @@
+# import necessary libraries
+from keras.preprocessing.image import ImageDataGenerator
+from keras.applications.vgg16 import VGG16
+from keras.layers import Dense, LSTM, Dropout, Flatten, Reshape
+from keras.models import Sequential
+
+# set up the data generators
+train_dir = 'X-ray Images/train'
+val_dir = 'X-ray Images/validation'
+test_dir = 'X-ray Images/test'
+img_height = 224
+img_width = 224
+batch_size = 32
+
+train_datagen = ImageDataGenerator(rescale=1./255)
+val_datagen = ImageDataGenerator(rescale=1./255)
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+train_generator = train_datagen.flow_from_directory(
+    train_dir,
+    target_size=(img_height, img_width),
+    batch_size=batch_size,
+    class_mode='categorical')
+
+val_generator = val_datagen.flow_from_directory(
+    val_dir,
+    target_size=(img_height, img_width),
+    batch_size=batch_size,
+    class_mode='categorical')
+
+test_generator = test_datagen.flow_from_directory(
+    test_dir,
+    target_size=(img_height, img_width),
+    batch_size=batch_size,
+    class_mode='categorical')
+
+# load the pre-trained VGG16 model and extract features from the images
+vgg_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))
+
+# freeze the layers in the VGG16 model
+for layer in vgg_model.layers:
+    layer.trainable = False
+
+# create the CNN-LSTM model
+model = Sequential()
+model.add(vgg_model)
+model.add(Flatten())
+model.add(Reshape((1, -1)))
+model.add(LSTM(256, return_sequences=False))
+model.add(Dropout(0.5))
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(train_generator.num_classes, activation='softmax'))
+
+# compile the model
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# train the model
+history = model.fit_generator(
+    train_generator,
+    steps_per_epoch=train_generator.n // batch_size,
+    epochs=10,
+    validation_data=val_generator,
+    validation_steps=val_generator.n // batch_size)
+
+# evaluate the model on the test data
+test_loss, test_acc = model.evaluate_generator(test_generator, steps=test_generator.n // batch_size)
+print('Test accuracy:', test_acc)
+
+# make predictions on new data
+predictions = model.predict_generator(test_generator, steps=test_generator.n // batch_size)