COVID-19：使用深度学习的医学诊断
介绍
正在进行的名为 COVID-19 的全球大流行是由 SARS-COV-2 引起的，该病毒传播迅速并发生变异，引发了几波疫情，主要影响第三世界和发展中国家。随着世界各国政府试图控制传播，受影响的人数正在稳步上升。
本文将使用 CoronaHack-Chest X 射线数据集。它包含胸部 X 射线图像，我们必须找到受冠状病毒影响的图像。
我们之前谈到的 SARS-COV-2 是主要影响呼吸系统的病毒类型，因此胸部 X 射线是我们可以用来识别受影响肺部的重要成像方法之一。这是一个并排比较：
如你所见，COVID-19 肺炎如何吞噬整个肺部，并且比细菌和病毒类型的肺炎更危险。
本文，将使用深度学习和迁移学习对受 Covid-19 影响的肺部的 X 射线图像进行分类和识别。
导入库和加载数据
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import numpy as np
import pandas as pd
sns.set()
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import Adam, SGD, RMSprop
from tensorflow.keras.applications import DenseNet121, VGG19, ResNet50
import PIL.Image
import matplotlib.pyplot as mpimg
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array
from tensorflow.keras.preprocessing import image
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
from sklearn.utils import shuffletrain_df = pd.read_csv('../input/coronahack-chest-xraydataset/Chest_xray_Corona_Metadata.csv')
train_df.shape
> (5910, 6)train_df.head(5)
train_df.info()
处理缺失值
missing_vals = train_df.isnull().sum()
missing_vals.plot(kind = 'bar')
train_df.dropna(how = 'all')
train_df.isnull().sum()
train_df.fillna('unknown', inplace=True)
train_df.isnull().sum()
train_data = train_df[train_df['Dataset_type'] == 'TRAIN']
test_data = train_df[train_df['Dataset_type'] == 'TEST']
assert train_data.shape[0] + test_data.shape[0] == train_df.shape[0]
print(f"Shape of train data : {train_data.shape}")
print(f"Shape of test data : {test_data.shape}")
test_data.sample(10)
我们将用“unknown”填充缺失值。
print((train_df['Label_1_Virus_category']).value_counts())
print('--------------------------')
print((train_df['Label_2_Virus_category']).value_counts())
因此标签 2 类别包含 COVID-19 案例
显示图像
test_img_dir = '/kaggle/input/coronahack-chest-xraydataset/Coronahack-Chest-XRay-Dataset/Coronahack-Chest-XRay-Dataset/test'
train_img_dir = '/kaggle/input/coronahack-chest-xraydataset/Coronahack-Chest-XRay-Dataset/Coronahack-Chest-XRay-Dataset/train'
sample_train_images = list(os.walk(train_img_dir))[0][2][:8]
sample_train_images = list(map(lambda x: os.path.join(train_img_dir, x), sample_train_images))
sample_test_images = list(os.walk(test_img_dir))[0][2][:8]
sample_test_images = list(map(lambda x: os.path.join(test_img_dir, x), sample_test_images))plt.figure(figsize = (10,10))
for iterator, filename in enumerate(sample_train_images):
image = PIL.Image.open(filename)
plt.subplot(4,2,iterator+1)
plt.imshow(image, cmap=plt.cm.bone)
plt.tight_layout()
可视化
plt.figure(figsize=(15,10))
sns.countplot(train_data['Label_2_Virus_category']);
对于 COVID-19 病例
fig, ax = plt.subplots(4, 2, figsize=(15, 10))
covid_path = train_data[train_data['Label_2_Virus_category']=='COVID-19']['X_ray_image_name'].values
sample_covid_path = covid_path[:4]
sample_covid_path = list(map(lambda x: os.path.join(train_img_dir, x), sample_covid_path))
for row, file in enumerate(sample_covid_path):
image = plt.imread(file)
ax[row, 0].imshow(image, cmap=plt.cm.bone)
ax[row, 1].hist(image.ravel(), 256, [0,256])
ax[row, 0].axis('off')
if row == 0:
ax[row, 0].set_title('Images')
ax[row, 1].set_title('Histograms')
fig.suptitle('Label 2 Virus Category = COVID-19', size=16)
plt.show()
对于正常情况
fig, ax = plt.subplots(4, 2, figsize=(15, 10))
normal_path = train_data[train_data['Label']=='Normal']['X_ray_image_name'].values
sample_normal_path = normal_path[:4]
sample_normal_path = list(map(lambda x: os.path.join(train_img_dir, x), sample_normal_path))
for row, file in enumerate(sample_normal_path):
image = plt.imread(file)
ax[row, 0].imshow(image, cmap=plt.cm.bone)
ax[row, 1].hist(image.ravel(), 256, [0,256])
ax[row, 0].axis('off')
if row == 0:
ax[row, 0].set_title('Images')
ax[row, 1].set_title('Histograms')
fig.suptitle('Label = NORMAL', size=16)
plt.show()
final_train_data = train_data[(train_data['Label'] == 'Normal') |
((train_data['Label'] == 'Pnemonia') &
(train_data['Label_2_Virus_category'] == 'COVID-19'))]final_train_data['class'] = final_train_data.Label.apply(lambda x: 'negative' if x=='Normal' else 'positive')
test_data['class'] = test_data.Label.apply(lambda x: 'negative' if x=='Normal' else 'positive')
final_train_data['target'] = final_train_data.Label.apply(lambda x: 0 if x=='Normal' else 1)
test_data['target'] = test_data.Label.apply(lambda x: 0 if x=='Normal' else 1)final_train_data = final_train_data[['X_ray_image_name', 'class', 'target', 'Label_2_Virus_category']]
final_test_data = test_data[['X_ray_image_name', 'class', 'target']]test_data['Label'].value_counts()
数据增强
datagen = ImageDataGenerator(
shear_range=0.2,
zoom_range=0.2,
)
def read_img(filename, size, path):
img = image.load_img(os.path.join(path, filename), target_size=size)
#convert image to array
img = image.img_to_array(img) / 255
return imgsamp_img = read_img(final_train_data['X_ray_image_name'][0],
(255,255),
train_img_path)
plt.figure(figsize=(10,10))
plt.suptitle('Data Augmentation', fontsize=28)
i = 0
for batch in datagen.flow(tf.expand_dims(samp_img,0), batch_size=6):
plt.subplot(3, 3, i+1)
plt.grid(False)
plt.imshow(batch.reshape(255, 255, 3));
if i == 8:
break
i += 1
plt.show();
corona_df = final_train_data[final_train_data['Label_2_Virus_category'] == 'COVID-19']
with_corona_augmented = []
def augment(name):
img = read_img(name, (255,255), train_img_path)
i = 0
for batch in tqdm(datagen.flow(tf.expand_dims(img, 0), batch_size=32)):
with_corona_augmented.append(tf.squeeze(batch).numpy())
if i == 20:
break
i =i+1
corona_df['X_ray_image_name'].apply(augment)注意：输出太长，无法包含在文章中。这是其中的一小部分。
train_arrays = []
final_train_data['X_ray_image_name'].apply(lambda x: train_arrays.append(read_img(x, (255,255), train_img_dir)))
test_arrays = []
final_test_data['X_ray_image_name'].apply(lambda x: test_arrays.append(read_img(x, (255,255), test_img_dir)))print(len(train_arrays))
print(len(test_arrays))
y_train = np.concatenate((np.int64(final_train_data['target'].values), np.ones(len(with_corona_augmented), dtype=np.int64)))将所有数据转换为张量
train_tensors = tf.convert_to_tensor(np.concatenate((np.array(train_arrays), np.array(with_corona_augmented))))
test_tensors = tf.convert_to_tensor(np.array(test_arrays))
y_train_tensor = tf.convert_to_tensor(y_train)
y_test_tensor = tf.convert_to_tensor(final_test_data['target'].values)
train_dataset = tf.data.Dataset.from_tensor_slices((train_tensors, y_train_tensor))
test_dataset = tf.data.Dataset.from_tensor_slices((test_tensors, y_test_tensor))for i,l in train_dataset.take(1):
plt.imshow(i);
生成批次
BATCH_SIZE = 16
BUFFER = 1000
train_batches = train_dataset.shuffle(BUFFER).batch(BATCH_SIZE)
test_batches = test_dataset.batch(BATCH_SIZE)
for i,l in train_batches.take(1):
print('Train Shape per Batch: ',i.shape);
for i,l in test_batches.take(1):
print('Test Shape per Batch: ',i.shape);
使用 ResNet50 进行迁移学习
INPUT_SHAPE = (255,255,3)
base_model = tf.keras.applications.ResNet50(input_shape= INPUT_SHAPE,
include_top=False,
weights='imagenet')
# We set it to False because we don't want to mess with the pretrained weights of the model.
base_model.trainable = False现在我们的迁移学习成功了！！
for i,l in train_batches.take(1):
pass
base_model(i).shape
> TensorShape([16, 8, 8, 2048])为图像分类添加密集层
model = Sequential()
model.add(base_model)
model.add(Layers.GlobalAveragePooling2D())
model.add(Layers.Dense(128))
model.add(Layers.Dropout(0.2))
model.add(Layers.Dense(1, activation = 'sigmoid'))
model.summary()
callbacks = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2)
model.compile(optimizer='adam',
loss = 'binary_crossentropy',
metrics=['accuracy'])预测
model.fit(train_batches, epochs=10, validation_data=test_batches, callbacks=[callbacks])
pred = model.predict_classes(np.array(test_arrays))# classification report
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(test_data['target'], pred.flatten()))
所以正如你所看到的，预测还不错。
我们将绘制一个混淆矩阵来可视化我们模型的性能：
con_mat = confusion_matrix(test_data['target'], pred.flatten())
plt.figure(figsize = (10,10))
plt.title('CONFUSION MATRIX')
sns.heatmap(con_mat, cmap='cividis',
yticklabels=['Negative', 'Positive'],
xticklabels=['Negative', 'Positive'],
annot=True);
尾注
这个数据集很有趣，学习数据科学和机器学习越多，就越觉得这个主题很有趣。如今，我们可以通过多种方式使用数据，使用数据可以挽救无数生命。
☆ END ☆
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