--- a
+++ b/training-models/cnn-model.py
@@ -0,0 +1,38 @@
+# importing dependencies
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# training and testing dataset directories path
+TRAIN_DATA_PATH = 'X-ray Images/train'
+TEST_DATA_PATH = 'X-ray Images/test'
+VALID_DATA_Path = 'X-ray Images/validation'
+# cnn-model architecture
+model = Sequential()
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
+model.add(MaxPooling2D((2, 2)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D((2, 2)))
+model.add(Conv2D(128, (3, 3), activation='relu'))
+model.add(MaxPooling2D((2, 2)))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(3, activation='softmax'))
+
+# compiling the model
+model.compile(optimizer='adam',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# data preprocessing
+train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+training_set = train_datagen.flow_from_directory(TRAIN_DATA_PATH, target_size=(224, 224), batch_size=32, class_mode='categorical')
+validation_set = test_datagen.flow_from_directory(VALID_DATA_Path, target_size=(224, 224), batch_size=32, class_mode='categorical')
+
+# Train the model
+history = model.fit(training_set, epochs=10, validation_data=validation_set)
+