This article is a review of the original Medium article (experiments are being conducted with changes to some conditions).
The field of application of neural networks in medicine is rapidly developing. In this area, tasks are being solved that facilitate the work of doctors. In particular, one of the demanded tasks in this area is the detection of objects in medical images (this is when a rectangle is superimposed on the picture, which limits the area in which there is supposedly some object). An example of such an image is shown below.
https://github.com/ultralytics/yolov3
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YOLOv3 , YOLO (You Only Look Once). , CNN (Convolutional Neural Network) ( ). YOLOv3 106- . , YOLOv3 ( 3), . YOLOv3:
https://www.researchgate.net/figure/The-framework-of-YOLOv3-neural-network-for-ship-detection_fig2_335228064
YOLO 13 13. ? , bounding box' ( ) , . (, ) confidence value ( ). , , (, ). YOLOv3.
https://medium.com/nerd-for-tech/a-real-time-object-detection-model-using-yolov3-algorithm-for-non-gpu-computers-8941a20b445
, YOLO , anchor boxes ( ). Medium. ( ) . , anchor boxes ( ) bounding box' COCO k-.
, . ? , . Kaggle, . .
. stage_2_train_images.zip stage_2_test_images.zip. , , . ( ) 26684 . DICOM 1024 1024. .
Class |
Target |
Patients |
Lung Opacity |
1 |
9555 |
No Lung Opacity / Not Normal |
0 |
11821 |
Normal |
0 |
8851 |
DICOM. JPG .
import pydicom as dicom
import os
from tqdm import tqdm
import numpy as np
import cv2
import pandas as pd
dicom jpg
def dicom_to_jpg(source_folder,destination_folder,labels):
images_path = os.listdir(source_folder)
image_dirs_label = {'image_dir':[],'Target':[]}
for n, image in tqdm(enumerate(images_path)):
ds = dicom.dcmread(os.path.join(source_folder, image))
pixel_array_numpy = ds.pixel_array
image = image.replace('.dcm', '.jpg')
cv2.imwrite(os.path.join(destination_folder, image), pixel_array_numpy)
image_dirs_label['image_dir'].append(os.path.join(destination_folder, image))
image_dirs_label['Target'].append(train_labels[train_labels.patientId== image.split('.')[0]].Target.values[0])
print('{} dicom files converted to jpg!'.format(len(images_path)))
return pd.DataFrame(image_dirs_label)
3 , : Normal — 0, No Lung Opacity / Not Normal — 0, Lung Opacity — 1. Class, Target Patients, , . .
, (positive Lung Opacity). (negative) 1:4 ( , ).
( , ). (positive) — . Albumentations. .
import albumentations as A
import pandas as pd
import cv2
import os
transformer
transform = A.Compose([
A.RandomRotate90(),
A.Flip(),
A.Transpose(),
A.OneOf([
A.IAAAdditiveGaussianNoise(),
A.GaussNoise(),
], p=0.2),
A.OneOf([
A.MotionBlur(p=.2),
A.MedianBlur(blur_limit=3, p=0.1),
A.Blur(blur_limit=3, p=0.1),
], p=0.2),
A.ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
A.OneOf([
A.OpticalDistortion(p=0.3),
A.GridDistortion(p=.1),
A.IAAPiecewiseAffine(p=0.3),
], p=0.2),
A.OneOf([
A.CLAHE(clip_limit=2),
A.IAASharpen(),
A.IAAEmboss(),
A.RandomBrightnessContrast(), ], p=0.3),
A.HueSaturationValue(p=0.3),
])
( " ", , )
"". JPG DICOM YOLOv3 (backbone') DarkNet. DarkNet . YOLOv3 ( Darknet) CheXNet. CheXNet 121- , , . CheXNet. 14 , , CheXNet 2- (negative — positive — ). TensorFlow, DenseNet121. .
# CheXNet classifier_weights.hdf5,
https://drive.google.com/file/d/1Bd50DpRWorGMDuEZ3-VHgndpJZwUGTAr/view
from absl import flags
from absl.flags import FLAGS
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.layers import (
Add,
Concatenate,
Conv2D,
Input,
Lambda,
LeakyReLU,
MaxPool2D,
UpSampling2D,
ZeroPadding2D,
BatchNormalization,
Dense
)
def base_model(chexnet_weights=None,size=None):
dense_net_121 = DenseNet121(input_shape = [size,size,3], include_top = False,pooling = 'avg')
base_model_output = Dense(units = 14, activation = 'relu')(dense_net_121.output)
base_model = Model(inputs = dense_net_121.input,outputs = base_model_output)
output_layer = Dense(1, activation = 'sigmoid')(base_model.layers[-2].output)
model = Model(inputs = base_model.inputs, outputs = output_layer)
if chexnet_weights:
model.load_weights(chexnet_weights)
final_base_model = Model(inputs = model.inputs, outputs = model.layers[-3].output)
return final_base_model
def ChexNet(name=None, chexnet_weights='PATH_TO_WEIGTHS/classifier_weights.hdf5',size=None):
chexnet = base_model(chexnet_weights = chexnet_weights, size = size)
back_bone = Model(inputs = chexnet.inputs, outputs=(chexnet.get_layer('pool3_conv').output,
chexnet.get_layer('pool4_conv').output,
chexnet.output),name=name)
return back_bone
:
Model |
Total params |
Trainable params |
Non-trainable params |
DarkNet |
61576342 |
61523734 |
52608 |
CheXNet |
27993206 |
27892662 |
100544 |
, CheXNet 2 , DarkNet. CheXNet.
YOLOv3 CheXNet ( ).
, (1 ) (positive negative), , ( positive). YOLOv3 416 416 13 13 (416 / 32 = 13). 13 13. anchor box' 3, 13 13 3- anchor box'. 13 13 3 = 507 ( ). , 507 . positive ( ) 2 (), 2 507-2=505 . , . , "" .
, ImageDataGenerator . , ( ), .
# true_augmented_labels - DataFrame,
( , ()
datagen=ImageDataGenerator(
rescale = 1. / 255.,
validation_split = 0.20)
train_generator = datagen.flow_from_dataframe(
dataframe = true_augmented_labels,
x_col = "image_dir",
y_col = "Target",
subset = "training",
batch_size = 4,
seed = 42,
shuffle = True,
class_mode = "binary",
target_size = (416, 416))
valid_generator = datagen.flow_from_dataframe(
dataframe = true_augmented_labels,
x_col = "image_dir",
y_col = "Target",
subset = "validation",
batch_size = 4,
seed = 42,
shuffle = True,
class_mode = "binary",
target_size = (416, 416))
( positive, negative), .
# brucechou1983_CheXNet_Keras_0.3.0_weights.h5 classifier_weights.hdf5
https://www.kaggle.com/theewok/chexnet-keras-weights/version/1
https://github.com/junaidnasirkhan/Replacing-YoloV3-Backbone-with-ChexNet-for-Pneumonia-Detection
dense_net_121 = DenseNet121(input_shape = [416,416] + [3], include_top = False, pooling = 'avg')
base_model_output = Dense(units = 14, activation = 'relu')(dense_net_121.output)
base_model = Model(inputs = dense_net_121.input, outputs = base_model_output)
""
base_model.load_weights('brucechou1983_CheXNet_Keras_0.3.0_weights.h5')
for layer in base_model.layers[:10]:
layer.trainable = False
output_layer = Dense(1, activation = 'sigmoid')(base_model.layers[-2].output)
model = Model(inputs = base_model.inputs, outputs = output_layer)
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy', f1_m])
checkpoint = ModelCheckpoint(filepath = 'classifier_weights.hdf5', monitor = 'val_accuracy', verbose = 0, save_best_only = True, save_weights_only = True, mode = 'auto')
log_dir = "classifier_logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard = TensorBoard(log_dir = log_dir, histogram_freq = 1, write_graph = True, write_grads = True)
callback_list = [checkpoint, tensorboard]
model.fit(train_generator,
validation_data = valid_generator,
epochs = 1, # 3
steps_per_epoch = len(train_generator),
callbacks = callback_list)
positive ( ).
# rsna_train_pos.tfrecord rsna_val_pos.tfrecord
.names ( )
"opacity" "no_opacity"
model = train(dataset = 'PATH_TO_TFRECORD/rsna_train_pos.tfrecord',
val_dataset = 'PATH_TO_TFRECORD/rsna_val_pos.tfrecord',
backbone = 'chexnet',
classes = 'PATH_TO_CLASSES/RSNA_VOC.names',
size = 416,
epochs = 30,
batch_size = 16, learning_rate = 1e-4,
num_classes = 1)
hdf5.
(YOLOv3 CheXNet).
learning_rate = 1e-4, epoch = 20
loss'
learning_rate = 1e-4, epochs = 30
loss'
.
CheXNet DarkNet , CheXNet, DarkNet.
CheXNet 1 , 20 30 , .
, epoch, .
. :
( , learning_rate)
CheXNet
To the original article on Medium
To my GitHub