initial commit

.gitignore

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+source/*
+test/*
+train/*

README.md

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+# Sample Convolutional NN to classify chest X-Rays
+
+## Requirements
+- Python 3.5+
+- Tensorflow
+- Pandas, PIL, Numpy
+
+
+
+# Data set and article reference:
+https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community

chestrays-keras.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import pathlib
+import shutil
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras import optimizers
+from keras.preprocessing.image import ImageDataGenerator
+
+# Parameters
+learning_rate = 0.01
+num_steps = 100
+#num_steps = 2000
+batch_size = 10
+display_step = 100
+
+# Network Parameters
+dropout = 0.5 # Dropout, probability to keep units
+
+# Images
+IMG_HEIGHT = 150
+IMG_WIDTH = 150
+CH = 3
+image_dir = "M:\\DataSets\\chestrays\\source\\" # XPS
+rows = 4001 # number 
+train_rows = 3600 # 90/10 split
+test_rows = 400
+
+
+df = pd.read_csv("chestrays.csv", header=None, na_values="?")
+df = df.iloc[1:rows]
+df.head()
+
+
+# Prepare train and test sets
+
+# Factorize the labels and make the directories, convert all | to _'s, remove spaces
+labels, names = pd.factorize(df[1])
+image_names = image_dir + df.iloc[0:rows,0].values
+d = dict() # dictionary of classification -> count pairs
+
+# data mover function, also populates the dictionary so we can see the distribution of data
+def copyImages(dataframe, idx, directory="train"):
+    classification = dataframe.iloc[idx][1].replace(" ","").replace("|","_")
+
+    if classification in d:
+        d[classification] += 1
+    else:
+        d[classification] = 1
+
+    source = image_dir + dataframe.iloc[idx][0]
+    destination = directory + "/" + classification
+    shutil.copy(source, destination)
+
+# Make train and test directories, replaces spaces and |'s with _
+for n in names:
+    dirname = n.replace(" ","").replace("|","_")
+    pathlib.Path("train/" + dirname).mkdir(parents=True, exist_ok=True)
+    pathlib.Path("test/" + dirname).mkdir(parents=True, exist_ok=True)
+
+
+for r in range(train_rows):
+    copyImages(df, r, "train")
+
+for r in range(test_rows):
+    copyImages(df, train_rows + r, "test")
+
+
+num_classes = len(list(set(labels)))
+
+print('Number of classes: {}'.format(num_classes))
+print('Number of rows: {}'.format(len(labels))) 
+print(names[:10])
+print(image_names)
+
+
+# Build the TF model
+model = Sequential()
+# input: 250x250 images with 1 channel
+# this applies 32 convolution filters of size 3x3 each.
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_WIDTH, IMG_HEIGHT, CH)))
+model.add(Conv2D(32, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(num_classes, activation='softmax'))
+
+sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd)
+
+
+# this is the augmentation configuration we will use for training
+train_datagen = ImageDataGenerator(rescale=1./255)
+
+# this is the augmentation configuration we will use for testing:
+# only rescaling
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+# this is a generator that will read pictures found in
+# subfolers of './train', and indefinitely generate
+# batches of augmented image data
+train_generator = train_datagen.flow_from_directory(
+        'train',  # this is the target directory
+        target_size=(IMG_WIDTH, IMG_HEIGHT),  # all images will be resized to 150x150
+        batch_size=batch_size,
+        class_mode='categorical')
+
+# this is a similar generator, for validation data
+validation_generator = test_datagen.flow_from_directory(
+        'test',
+        target_size=(IMG_WIDTH, IMG_HEIGHT),
+        batch_size=batch_size,
+        class_mode='categorical')
+
+
+model.fit_generator(
+        train_generator,
+        steps_per_epoch=num_steps,
+        epochs=50,
+        validation_data=validation_generator,
+        validation_steps=800)
+