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* Remove python_version < '3.12' for tensorflow * Reenable dynamic_programming/k_means_clustering_tensorflow.py * updating DIRECTORY.md * Try to fix ruff * Try to fix ruff * Try to fix ruff * Try to fix ruff * Try to fix ruff * Reenable machine_learning/lstm/lstm_prediction.py * updating DIRECTORY.md * Try to fix ruff * Reenable computer_vision/cnn_classification.py * updating DIRECTORY.md * Reenable neural_network/input_data.py * updating DIRECTORY.md * Try to fix ruff * Try to fix ruff * Try to fix mypy * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try to fix ruff * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: MaximSmolskiy <MaximSmolskiy@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
"""
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Convolutional Neural Network
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Objective : To train a CNN model detect if TB is present in Lung X-ray or not.
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Resources CNN Theory :
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https://en.wikipedia.org/wiki/Convolutional_neural_network
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Resources Tensorflow : https://www.tensorflow.org/tutorials/images/cnn
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Download dataset from :
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https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html
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1. Download the dataset folder and create two folder training set and test set
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in the parent dataset folder
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2. Move 30-40 image from both TB positive and TB Negative folder
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in the test set folder
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3. The labels of the images will be extracted from the folder name
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the image is present in.
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"""
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# Part 1 - Building the CNN
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import numpy as np
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# Importing the Keras libraries and packages
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import tensorflow as tf
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from tensorflow.keras import layers, models
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if __name__ == "__main__":
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# Initialising the CNN
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# (Sequential- Building the model layer by layer)
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classifier = models.Sequential()
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# Step 1 - Convolution
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# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
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# (3,3) is the kernel size (filter matrix)
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classifier.add(
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layers.Conv2D(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
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)
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# Step 2 - Pooling
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classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))
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# Adding a second convolutional layer
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classifier.add(layers.Conv2D(32, (3, 3), activation="relu"))
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classifier.add(layers.MaxPooling2D(pool_size=(2, 2)))
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# Step 3 - Flattening
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classifier.add(layers.Flatten())
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# Step 4 - Full connection
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classifier.add(layers.Dense(units=128, activation="relu"))
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classifier.add(layers.Dense(units=1, activation="sigmoid"))
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# Compiling the CNN
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classifier.compile(
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optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
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)
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# Part 2 - Fitting the CNN to the images
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# Load Trained model weights
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# from keras.models import load_model
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# regressor=load_model('cnn.h5')
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train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
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rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
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)
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test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
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training_set = train_datagen.flow_from_directory(
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"dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
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)
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test_set = test_datagen.flow_from_directory(
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"dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
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)
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classifier.fit_generator(
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training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
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)
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classifier.save("cnn.h5")
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# Part 3 - Making new predictions
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test_image = tf.keras.preprocessing.image.load_img(
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"dataset/single_prediction/image.png", target_size=(64, 64)
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)
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test_image = tf.keras.preprocessing.image.img_to_array(test_image)
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test_image = np.expand_dims(test_image, axis=0)
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result = classifier.predict(test_image)
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# training_set.class_indices
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if result[0][0] == 0:
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prediction = "Normal"
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if result[0][0] == 1:
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prediction = "Abnormality detected"
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