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Pneumonia Detection using CNN with Implementation in Python

Introduction

Pneumonia CNN

Several x-ray images in the dataset used in this project.

Loading modules and train images

import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_model
from keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten
from keras.preprocessing.image import ImageDataGeneratornp.random.seed(22)
# Do not forget to include the last slash
def load_normal(norm_path):
    norm_files = np.array(os.listdir(norm_path))
    norm_labels = np.array(['normal']*len(norm_files))
    
    norm_images = []
    for image in tqdm(norm_files):
        image = cv2.imread(norm_path + image)
        image = cv2.resize(image, dsize=(200,200))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        norm_images.append(image)
        
    norm_images = np.array(norm_images)
    
    return norm_images, norm_labels
def load_pneumonia(pneu_path):
    pneu_files = np.array(os.listdir(pneu_path))
    pneu_labels = np.array([pneu_file.split('_')[1] for pneu_file in pneu_files])
    
    pneu_images = []
    for image in tqdm(pneu_files):
        image = cv2.imread(pneu_path + image)
        image = cv2.resize(image, dsize=(200,200))
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        pneu_images.append(image)
        
    pneu_images = np.array(pneu_images)
    
    return pneu_images, pneu_labels
norm_images, norm_labels = load_normal('/kaggle/input/chest-xray-pneumonia/chest_xray/train/NORMAL/')pneu_images, pneu_labels = load_pneumonia('/kaggle/input/chest-xray-pneumonia/chest_xray/train/PNEUMONIA/')

Pneumonia CNN

The progress bar displayed using tqdm module.
X_train = np.append(norm_images, pneu_images, axis=0)
y_train = np.append(norm_labels, pneu_labels)

Pneumonia CNN

The shape of the features (X) and labels (y).

Pneumonia CNN

Finding out the number of unique values in our training set

Displaying several images

fig, axes = plt.subplots(ncols=7, nrows=2, figsize=(16, 4))

indices = np.random.choice(len(X_train), 14)
counter = 0

for i in range(2):
    for j in range(7):
        axes[i,j].set_title(y_train[indices[counter]])
        axes[i,j].imshow(X_train[indices[counter]], cmap='gray')
        axes[i,j].get_xaxis().set_visible(False)
        axes[i,j].get_yaxis().set_visible(False)
        counter += 1
plt.show()

Image for post

Some of the preprocessed x-ray images.

Loading test images

norm_images_test, norm_labels_test = load_normal('/kaggle/input/chest-xray-pneumonia/chest_xray/test/NORMAL/')pneu_images_test, pneu_labels_test = load_pneumonia('/kaggle/input/chest-xray-pneumonia/chest_xray/test/PNEUMONIA/')X_test = np.append(norm_images_test, pneu_images_test, axis=0)
y_test = np.append(norm_labels_test, pneu_labels_test)
# Use this to save variables
with open('pneumonia_data.pickle', 'wb') as f:
    pickle.dump((X_train, X_test, y_train, y_test), f)# Use this to load variables
with open('pneumonia_data.pickle', 'rb') as f:
    (X_train, X_test, y_train, y_test) = pickle.load(f)

Label preprocessing

y_train = y_train[:, np.newaxis]
y_test = y_test[:, np.newaxis]
one_hot_encoder = OneHotEncoder(sparse=False)
y_train_one_hot = one_hot_encoder.fit_transform(y_train)
y_test_one_hot = one_hot_encoder.transform(y_test)

 

Reshaping X data into (None, 200, 200, 1)

X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)

Data augmentation

datagen = ImageDataGenerator(
        rotation_range = 10,  
        zoom_range = 0.1, 
        width_shift_range = 0.1, 
        height_shift_range = 0.1)
datagen.fit(X_train)train_gen = datagen.flow(X_train, y_train_one_hot, batch_size=32)

 

CNN (Convolutional Neural Network)

input1 = Input(shape=(X_train.shape[1], X_train.shape[2], 1))

cnn = Conv2D(16, (3, 3), activation='relu', strides=(1, 1), 
    padding='same')(input1)
cnn = Conv2D(32, (3, 3), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = MaxPool2D((2, 2))(cnn)

cnn = Conv2D(16, (2, 2), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = Conv2D(32, (2, 2), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = MaxPool2D((2, 2))(cnn)

cnn = Flatten()(cnn)
cnn = Dense(100, activation='relu')(cnn)
cnn = Dense(50, activation='relu')(cnn)
output1 = Dense(3, activation='softmax')(cnn)

model = Model(inputs=input1, outputs=output1)

Image for post

Summary of the CNN model.
model.compile(loss='categorical_crossentropy', 
              optimizer='adam', metrics=['acc'])
history = model.fit_generator(train_gen, epochs=30, 
          validation_data=(X_test, y_test_one_hot))
Epoch 1/30
163/163 [==============================] - 19s 114ms/step - loss: 5.7014 - acc: 0.6133 - val_loss: 0.7971 - val_acc: 0.7228
.
.
.
Epoch 10/30
163/163 [==============================] - 18s 111ms/step - loss: 0.5575 - acc: 0.7650 - val_loss: 0.8788 - val_acc: 0.7308
.
.
.
Epoch 20/30
163/163 [==============================] - 17s 102ms/step - loss: 0.5267 - acc: 0.7784 - val_loss: 0.6668 - val_acc: 0.7917
.
.
.
Epoch 30/30
163/163 [==============================] - 17s 104ms/step - loss: 0.4915 - acc: 0.7922 - val_loss: 0.7079 - val_acc: 0.8045
plt.figure(figsize=(8,6))
plt.title('Accuracy scores')
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.legend(['acc', 'val_acc'])
plt.show()plt.figure(figsize=(8,6))
plt.title('Loss value')
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['loss', 'val_loss'])
plt.show()

Image for post

Accuracy score improvement

Image for post

Loss value decrease.

 

Model evaluation

predictions = model.predict(X_test)
predictions = one_hot_encoder.inverse_transform(predictions)
cm = confusion_matrix(y_test, predictions)
classnames = ['bacteria', 'normal', 'virus']plt.figure(figsize=(8,8))
plt.title('Confusion matrix')
sns.heatmap(cm, cbar=False, xticklabels=classnames, yticklabels=classnames, fmt='d', annot=True, cmap=plt.cm.Blues)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()

Image for post

Confusion matrix constructed based on test data.
import os
import cv2
import pickle	# Used to save variables
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm	# Used to display progress bar
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_model
from keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten
from keras.preprocessing.image import ImageDataGenerator	# Used to generate images

np.random.seed(22)

# Do not forget to include the last slash
def load_normal(norm_path):
    norm_files = np.array(os.listdir(norm_path))
    norm_labels = np.array(['normal']*len(norm_files))
    
    norm_images = []
    for image in tqdm(norm_files):
		# Read image
        image = cv2.imread(norm_path + image)
		# Resize image to 200x200 px
        image = cv2.resize(image, dsize=(200,200))
		# Convert to grayscale
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        norm_images.append(image)
        
    norm_images = np.array(norm_images)
    
    return norm_images, norm_labels

def load_pneumonia(pneu_path):
    pneu_files = np.array(os.listdir(pneu_path))
    pneu_labels = np.array([pneu_file.split('_')[1] for pneu_file in pneu_files])
    
    pneu_images = []
    for image in tqdm(pneu_files):
		# Read image
        image = cv2.imread(pneu_path + image)
		# Resize image to 200x200 px
        image = cv2.resize(image, dsize=(200,200))
		# Convert to grayscale
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        pneu_images.append(image)
        
    pneu_images = np.array(pneu_images)
    
    return pneu_images, pneu_labels


print('Loading images')
# All images are stored in _images, all labels are in _labels
norm_images, norm_labels = load_normal('/kaggle/input/chest-xray-pneumonia/chest_xray/train/NORMAL/')
pneu_images, pneu_labels = load_pneumonia('/kaggle/input/chest-xray-pneumonia/chest_xray/train/PNEUMONIA/')

# Put all train images to X_train 
X_train = np.append(norm_images, pneu_images, axis=0)

# Put all train labels to y_train
y_train = np.append(norm_labels, pneu_labels)

print(X_train.shape)
print(y_train.shape)
# Finding out the number of samples of each class
print(np.unique(y_train, return_counts=True))

print('Display several images')
fig, axes = plt.subplots(ncols=7, nrows=2, figsize=(16, 4))

indices = np.random.choice(len(X_train), 14)
counter = 0

for i in range(2):
    for j in range(7):
        axes[i,j].set_title(y_train[indices[counter]])
        axes[i,j].imshow(X_train[indices[counter]], cmap='gray')
        axes[i,j].get_xaxis().set_visible(False)
        axes[i,j].get_yaxis().set_visible(False)
        counter += 1
plt.show()


print('Loading test images')
# Do the exact same thing as what we have done on train data
norm_images_test, norm_labels_test = load_normal('/kaggle/input/chest-xray-pneumonia/chest_xray/test/NORMAL/')
pneu_images_test, pneu_labels_test = load_pneumonia('/kaggle/input/chest-xray-pneumonia/chest_xray/test/PNEUMONIA/')
X_test = np.append(norm_images_test, pneu_images_test, axis=0)
y_test = np.append(norm_labels_test, pneu_labels_test)

# Save the loaded images to pickle file for future use
with open('pneumonia_data.pickle', 'wb') as f:
    pickle.dump((X_train, X_test, y_train, y_test), f)

# Here's how to load it
with open('pneumonia_data.pickle', 'rb') as f:
    (X_train, X_test, y_train, y_test) = pickle.load(f)

print('Label preprocessing')

# Create new axis on all y data
y_train = y_train[:, np.newaxis]
y_test = y_test[:, np.newaxis]

# Initialize OneHotEncoder object
one_hot_encoder = OneHotEncoder(sparse=False)

# Convert all labels to one-hot
y_train_one_hot = one_hot_encoder.fit_transform(y_train)
y_test_one_hot = one_hot_encoder.transform(y_test)

print('Reshaping X data')
# Reshape the data into (no of samples, height, width, 1), where 1 represents a single color channel
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)

print('Data augmentation')
# Generate new images with some randomness
datagen = ImageDataGenerator(
		rotation_range = 10,  
        zoom_range = 0.1, 
        width_shift_range = 0.1, 
        height_shift_range = 0.1)

datagen.fit(X_train)
train_gen = datagen.flow(X_train, y_train_one_hot, batch_size = 32)

print('CNN')

# Define the input shape of the neural network
input_shape = (X_train.shape[1], X_train.shape[2], 1)
print(input_shape)

input1 = Input(shape=input_shape)

cnn = Conv2D(16, (3, 3), activation='relu', strides=(1, 1), 
    padding='same')(input1)
cnn = Conv2D(32, (3, 3), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = MaxPool2D((2, 2))(cnn)

cnn = Conv2D(16, (2, 2), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = Conv2D(32, (2, 2), activation='relu', strides=(1, 1), 
    padding='same')(cnn)
cnn = MaxPool2D((2, 2))(cnn)

cnn = Flatten()(cnn)
cnn = Dense(100, activation='relu')(cnn)
cnn = Dense(50, activation='relu')(cnn)
output1 = Dense(3, activation='softmax')(cnn)

model = Model(inputs=input1, outputs=output1)

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

# Using fit_generator() instead of fit() because we are going to use data
# taken from the generator. Note that the randomness is changing
# on each epoch
history = model.fit_generator(train_gen, epochs=30, 
          validation_data=(X_test, y_test_one_hot))

# Saving model
model.save('pneumonia_cnn.h5')

print('Displaying accuracy')
plt.figure(figsize=(8,6))
plt.title('Accuracy scores')
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.legend(['acc', 'val_acc'])
plt.show()

print('Displaying loss')
plt.figure(figsize=(8,6))
plt.title('Loss value')
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['loss', 'val_loss'])
plt.show()

# Predicting test data
predictions = model.predict(X_test)
print(predictions)

predictions = one_hot_encoder.inverse_transform(predictions)

print('Model evaluation')
print(one_hot_encoder.categories_)

classnames = ['bacteria', 'normal', 'virus']

# Display confusion matrix
cm = confusion_matrix(y_test, predictions)
plt.figure(figsize=(8,8))
plt.title('Confusion matrix')
sns.heatmap(cm, cbar=False, xticklabels=classnames, yticklabels=classnames, fmt='d', annot=True, cmap=plt.cm.Blues)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()

References

About the Author

Pneumonia CNN

Muhammad Ardi

A computer science undergraduate student of Universitas Gadjah Mada, Yogyakarta, Indonesia. I’m a big fan of the machine and deep learning stuff.

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