cnn.py

import os
import imageio
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from keras.callbacks import EarlyStopping
from keras import backend as K
import skimage
from sklearn.model_selection import train_test_split
import pydicom

os.environ["CUDA_VISIBLE_DEVICES"]="0"

classifier = Sequential()
classifier.add(Conv2D(64, (5, 5), input_shape = (256, 256, 1), activation = 'relu'))
classifier.add(Dropout(0.2))
classifier.add(MaxPooling2D(pool_size = (4, 4)))
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(Dropout(0.2))
classifier.add(MaxPooling2D(pool_size = (4, 4)))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dropout(0.3))
classifier.add(Dense(units = 32, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

X = []
y = []

dir = "./Data/Patches/NoPlaque"
for filename in os.listdir(dir):
	print(os.path.join(dir, filename))
	img = np.array(imageio.imread(os.path.join(dir, filename)))[:,:,0]
	#img = np.load(os.path.join(dir, filename))[:,:,0]
	X.append(img)
	y.append(0)

dir = "./Data/Patches/Plaque/Aug"
for filename in os.listdir(dir):
	print(os.path.join(dir, filename))
	img = np.load(os.path.join(dir, filename))[:,:,0]
	#img = np.load(os.path.join(dir, filename))[:,:,0]
	X.append(img)
	y.append(1)

X = np.array(X)
y = np.array(y)

Xtr, Xts, ytr, yts = train_test_split(X, y, test_size=0.25, random_state=42)

print(type(Xtr))
classifier.summary()
es = EarlyStopping(patience=20, restore_best_weights = True) 

history = classifier.fit(Xtr, ytr,
               callbacks = [es],
               epochs = 40,
               batch_size = 8,
               validation_data = (Xts, yts))
classifier.evaluate(Xts, yts)
classifier.save("./Models/model.h5")
import pickle as pickle
pickle.dump(history, open("../Models/history.pkl", "wb"))