FilesExpand file tree

master

/

cnn_classification.py

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

101 lines (74 loc) · 3.12 KB

master

/

cnn_classification.py

Copy path

Top

File metadata and controls

• Code
• Blame

101 lines (74 loc) · 3.12 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

"""

Convolutional Neural Network

Objective : To train a CNN model detect if TB is present in Lung X-ray or not.

Resources CNN Theory :

https://en.wikipedia.org/wiki/Convolutional_neural_network

Resources Tensorflow : https://www.tensorflow.org/tutorials/images/cnn

Download dataset from :

https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html

1. Download the dataset folder and create two folder training set and test set

in the parent dataste folder

2. Move 30-40 image from both TB positive and TB Negative folder

in the test set folder

3. The labels of the iamges will be extracted from the folder name

the image is present in.

"""

# Part 1 - Building the CNN

import numpy as np

# Importing the Keras libraries and packages

import tensorflow as tf

from tensorflow.keras import layers, models

if __name__ == "__main__":

# Initialising the CNN

# (Sequential- Building the model layer by layer)

classifier = models.Sequential()

# Step 1 - Convolution

# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel

# (3,3) is the kernel size (filter matrix)

classifier.add(

layers.Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation="relu")

)

# Step 2 - Pooling

classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))

# Adding a second convolutional layer

classifier.add(layers.Conv2D(32, (3, 3), activation="relu"))

classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))

# Step 3 - Flattening

classifier.add(layers.Flatten())

# Step 4 - Full connection

classifier.add(layers.Dense(units=128, activation="relu"))

classifier.add(layers.Dense(units=1, activation="sigmoid"))

# Compiling the CNN

classifier.compile(

optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]

)

# Part 2 - Fitting the CNN to the images

# Load Trained model weights

# from keras.models import load_model

# regressor=load_model('cnn.h5')

train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(

rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True

)

test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

training_set = train_datagen.flow_from_directory(

"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"

)

test_set = test_datagen.flow_from_directory(

"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"

)

classifier.fit_generator(

training_set, steps_per_epoch=5, epochs=30, validation_data=test_set

)

classifier.save("cnn.h5")

# Part 3 - Making new predictions

test_image = tf.keras.preprocessing.image.load_img(

"dataset/single_prediction/image.png", target_size=(64, 64)

)

test_image = tf.keras.preprocessing.image.img_to_array(test_image)

test_image = np.expand_dims(test_image, axis=0)

result = classifier.predict(test_image)

training_set.class_indices

if result[0][0] == 0:

prediction = "Normal"

if result[0][0] == 1:

prediction = "Abnormality detected"