chest_xray_prediction.py

from keras.preprocessing.image import ImageDataGenerator, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input
from keras.models import Model
from keras.optimizers import Adam
import os
import pandas as pd
import numpy as np
import keras
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
# %matplotlib inline

import os
import zipfile
for file in os.listdir():
    zip_ref = zipfile.ZipFile('chest-xray-pneumonia.zip', 'r')
    zip_ref.extractall()
    zip_ref.close()

#giving path of the dataset
print(os.listdir("chest_xray"))

print(os.listdir("chest_xray/train"))

print(os.listdir("chest_xray/test"))

#setting image width and height to give input to the CNN
img_width, img_height = 150,150

#creating testing and training data 
train_data_dir = 'chest_xray/train'
validation_data_dir = 'chest_xray/val'
test_data_dir = 'chest_xray/test'


if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.layers

#values of the data are defined
training_samples = 5216
validation_samples = 16
epochs = 10
batch_size = 16

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

# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

test_generator = test_datagen.flow_from_directory(
    test_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=training_samples//validation_samples,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=validation_samples//batch_size)

scores = model.evaluate_generator(validation_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))
scores = model.evaluate_generator(train_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))
scores = model.evaluate_generator(test_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))