pneumonia_detection.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import warnings
warnings.filterwarnings('ignore')

import tensorflow as tf
from tensorflow import keras
from keras.layers import Input, Lambda, Dense, Flatten
from keras.models import Model
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
import numpy as np
from glob import glob
import matplotlib.pyplot as plt
import pickle


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IMAGE_SIZE = [224, 224]

train_path = 'train'
valid_path = 'test'


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vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)


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for layer in vgg.layers:
    layer.trainable = False


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folders = glob('train/*')
x = Flatten()(vgg.output)


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prediction = Dense(len(folders), activation='softmax')(x)
model = Model(inputs=vgg.input, outputs=prediction)
model.summary()


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


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from keras.preprocessing.image import ImageDataGenerator


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

test_datagen = ImageDataGenerator(rescale = 1./255)
valid_datagen = ImageDataGenerator(rescale = 1./255)


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training_set = train_datagen.flow_from_directory('chest_xray/train',
                                                 target_size = (224, 224),
                                                 batch_size = 10,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('chest_xray/test',
                                            target_size = (224, 224),
                                            batch_size = 10,
                                            class_mode = 'categorical')

valid_set = valid_datagen.flow_from_directory('chest_xray/val',
                                            target_size = (224, 224),
                                            batch_size = 10,
                                            class_mode = 'categorical')


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r = model.fit_generator(
  training_set,
  validation_data=test_set,
  epochs=1,
  steps_per_epoch=len(training_set),
  validation_steps=len(valid_set)
)


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import tensorflow as tf
from keras.models import load_model

model.save('chest_xray.h5')


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from keras.models import load_model


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from keras.preprocessing import image
import tensorflow.keras.utils as utils
from keras.applications.vgg16 import preprocess_input


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import numpy as np


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model=load_model('chest_xray.h5')


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img=utils.load_img('test/NORMAL/NORMAL2-IM-0023-0001.jpeg',target_size=(224,224))
x=utils.img_to_array(img)
x=np.expand_dims(x, axis=0)
img_data=preprocess_input(x)
classes=model.predict(img_data)


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result=int(classes[0][0])


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result


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if result==0:
    print("Person is affeected by pneumonia")
else:
    print("Result is normal")


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