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resnet_architecture.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Developed by: Ivan Legorreta
Contact information: ilegorreta@outlook.com
'''
import nibabel as nib
import os
import numpy as np
import pandas as pd
import h5py
import time
import csv
from tensorflow import keras
import tensorflow as tf
from keras.utils import to_categorical
from keras.layers import Conv3D, MaxPool3D, Flatten, Dense, MaxPooling3D, GlobalAveragePooling3D, Add
from keras.layers import Dropout, Input, BatchNormalization, Activation
from sklearn.metrics import confusion_matrix, accuracy_score
from keras.losses import mean_squared_error, huber_loss
from keras.optimizers import Adadelta, SGD, Adam
from keras.models import Model, Sequential
from keras.utils import to_categorical
from keras.utils.io_utils import HDF5Matrix
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.model_selection import StratifiedKFold, KFold
from keras import backend as K
started_at = time.asctime()
from scipy.stats import spearmanr
#Identity Block for ResNet
def id_block(X, f):
X_shortcut = X
#First component of main path
X = BatchNormalization() (X)
X = Activation('relu')(X)
X = Conv3D(filters=int(f/4), kernel_size=1, kernel_initializer='he_uniform', padding='same') (X)
#Second component of main path
X = BatchNormalization() (X)
X = Activation('relu')(X)
X = Conv3D(filters=int(f/4), kernel_size=3, kernel_initializer='he_uniform', padding='same') (X)
#Third component of main path
X = BatchNormalization() (X)
X = Activation('relu')(X)
X = Conv3D(filters=f, kernel_size=1, kernel_initializer='he_uniform', padding='same') (X)
#Merge paths with skip connection
X = Add()([X, X_shortcut])
return X
def model_architecture():
input_imgs = Input(shape=(217, 178, 60, 1))
conv2 = Conv3D(filters=2, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (input_imgs)
maxPool1 = MaxPool3D(pool_size=(2, 2, 2), strides=(2,2,2)) (conv2)
bn4 = BatchNormalization() (maxPool1)
act4 = Activation('relu') (bn4)
conv4 = Conv3D(filters=4, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act4)
maxPool2 = MaxPool3D(pool_size=(2, 2, 2), strides=(2,2,2)) (conv4)
bn8 = BatchNormalization() (maxPool2)
act8 = Activation('relu') (bn8)
conv8 = Conv3D(filters=8, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act8)
bn16 = BatchNormalization() (conv8)
act16 = Activation('relu') (bn16)
conv16 = Conv3D(filters=16, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act16)
conv_block16 = id_block(conv16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
conv_block16 = id_block(conv_block16, 16)
maxPool3 = MaxPool3D(pool_size=(2, 2, 2), strides=(2,2,2)) (conv_block16)
bn32 = BatchNormalization() (maxPool3)
act32 = Activation('relu') (bn32)
conv32 = Conv3D(filters=32, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act32)
bn64 = BatchNormalization() (conv32)
act64 = Activation('relu') (bn64)
conv64 = Conv3D(filters=64, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act64)
maxPool4 = MaxPool3D(pool_size=(2, 2, 2), strides=(2,2,2)) (conv64)
bn128 = BatchNormalization() (maxPool4)
act128 = Activation('relu') (bn128)
conv128 = Conv3D(filters=128, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act128)
bn256 = BatchNormalization() (conv128)
act256 = Activation('relu') (bn256)
conv256 = Conv3D(filters=256, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act256)
bn512 = BatchNormalization() (conv256)
act512 = Activation('relu') (bn512)
conv512 = Conv3D(filters=512, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act512)
conv_block512 = id_block(conv512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
conv_block512 = id_block(conv_block512, 512)
bn1024 = BatchNormalization() (conv_block512)
act1024 = Activation('relu') (bn1024)
conv1024 = Conv3D(filters=1024, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act1024)
conv_block1024 = id_block(conv1024, 1024)
conv_block1024 = id_block(conv_block1024, 1024)
conv_block1024 = id_block(conv_block1024, 1024)
conv_block1024 = id_block(conv_block1024, 1024)
conv_block1024 = id_block(conv_block1024, 1024)
conv_block1024 = id_block(conv_block1024, 1024)
bn2048 = BatchNormalization() (conv_block1024)
act2048 = Activation('relu') (bn2048)
conv2048 = Conv3D(filters=2048, kernel_size=(3, 3, 3), kernel_initializer='he_uniform', padding='same') (act2048)
conv_block2048 = id_block(conv2048, 2048)
conv_block2048 = id_block(conv_block2048, 2048)
conv_block2048 = id_block(conv_block2048, 2048)
conv_block2048 = id_block(conv_block2048, 2048)
conv_block2048 = id_block(conv_block2048, 2048)
conv_block2048 = id_block(conv_block2048, 2048)
conv_block2048 = BatchNormalization() (conv_block2048)
conv_block2048 = Activation('relu') (conv_block2048)
globalAP = GlobalAveragePooling3D() (conv_block2048)
dense1 = Dense(units=256, activation='relu', kernel_initializer='he_uniform') (globalAP)
dp = Dropout(0.4) (dense1)
dense2 = Dense(units=1, activation='linear') (dp)
model = Model(inputs=input_imgs, outputs=dense2)
return model