itstalmeez/pneumonia_vs_normal

import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from google.colab import drive drive.mount('/content/drive')

Define constants

image_size = (150, 150) batch_size = 32

Data augmentation for the training set

train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True )

Rescaling for the testing set

test_datagen = ImageDataGenerator(rescale=1./255)

Load the training set

train_set = train_datagen.flow_from_directory( '/content/drive/MyDrive/chest_xray/train', target_size=image_size, batch_size=batch_size, class_mode='binary' )

Load the testing set

test_set = test_datagen.flow_from_directory( '/content/drive/MyDrive/chest_xray/test', target_size=image_size, batch_size=batch_size, class_mode='binary' )

Build the CNN model

model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(image_size[0], image_size[1], 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(units=128, activation='relu')) model.add(Dense(units=1, activation='sigmoid'))

Compile the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train the model

model.fit(train_set, epochs=10, validation_data=test_set)

Save the model

model.save('pneumonia_model.h5')

Evaluate the model on the testing set

accuracy = model.evaluate(test_set)[1] print(f'Test Accuracy: {accuracy}')

Make predictions on new images

def predict_image(file_path): img = tf.keras.preprocessing.image.load_img(file_path, target_size=image_size) img_array = tf.keras.preprocessing.image.img_to_array(img) img_array = tf.expand_dims(img_array, 0) # Create a batch

predictions = model.predict(img_array)
if predictions[0] > 0.5:
    print("Prediction: Pneumonia")
else:
    print("Prediction: Normal")

Example usage:

image_path = "/content/drive/MyDrive/chest_xray/train/PNEUMONIA/BACTERIA-1033441-0001.jpeg" predict_image(image_path)