pneumonia_cnn.py

### Pneumonia Detection using CNN
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import models, layers

#def dir_file_count(directory):
#    return sum([len(files) for r, d, files in os.walk(directory)])
	
num_classes = 2 # number of folders under data/chest_xray
target_size = (224,224)

# Dataset Chest_Xray_Pnenumonia
train_dir = 'datasets/chest_xray/train'
val_dir   = 'datasets/chest_xray/val'

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

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=target_size,
    class_mode='categorical',
    batch_size=32,
    color_mode="grayscale",	
    shuffle=True)
	
validation_datagen = ImageDataGenerator(rescale=rescale)

validation_generator = validation_datagen.flow_from_directory(
    val_dir,
    target_size=target_size,
    class_mode='categorical',
    batch_size=8,
    color_mode="grayscale",
    shuffle = False)
		
# Build Model
model = models.Sequential()
# block 1
model.add(layers.Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu', input_shape=(224,224,1)))
model.add(layers.Conv2D(16, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
# block 2
model.add(layers.Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(32, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
# block 3
model.add(layers.Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(64, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
# block 4
model.add(layers.Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(96, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
# block 5
model.add(layers.Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.Conv2D(128, kernel_size=(3, 3), padding='same', activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
# fully-connected layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dropout(0.4))
model.add(layers.Dense(num_classes , activation='softmax'))

model.summary()

# Compile Model
model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])

# Train Model 
num_epochs= 100
STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VAL   = validation_generator.n // validation_generator.batch_size
model.fit(train_generator, steps_per_epoch=STEP_SIZE_TRAIN, epochs=num_epochs, validation_data=validation_generator, validation_steps=STEP_SIZE_VAL)
			
# Save Model
models.save_model(model, 'models/pneumonia_cnn.h5')