mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-11-23 21:11:08 +00:00
2899cdac20
* cnn classification file * black formatted * flake8 corrected * added cnn classification * Delete requirements.txt * Update cnn_classification.py * Create cnn_classification.py * using keras from tensorflow only * update tensorflow * Update cnn_classification.py * Delete computer_vision/cnn_classification directory
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
"""
|
|
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
|
|
classifier = models.Sequential()
|
|
|
|
# Step 1 - Convolution
|
|
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"
|