Скачать или смотреть Detecting COVID-19 From Chest X-Ray| Training a Convolutional Neural Network| Deep Learning| VGG16

Detecting COVID-19 in X-ray images with Keras, TensorFlow, and Deep Learning.In this tutorial, you will learn how to automatically detect COVID-19 in a hand-created X-ray image dataset using Keras, TensorFlow, and Deep Learning.

NOTE: YOU MUST TAKE MORE THAN 25 EPOCHES FOR GOOD ACCURACY

-------------------------------------------------------------------------------------------------------------
ALL CODE AND DATA SET IS AVAILABLE HERE: https://github.com/anandraj8756/COVID...
----------------------------------------------------------------------------------------------------------------

we will discuss how you can make your own machine learning model to successfully detect COVID-19 from Chest X-Rays by building a Simple Convolution Neural Network.

-------------------------------------------------------------------------------------------------------
Dataset Preparation
Understanding the Dataset
Building a CNN
Model Training
-------------------------------------------------------------------------------------------------------

1. Sample an open source dataset of X-ray images for patients who have tested positive for COVID-19
2. Sample “normal” (i.e., not infected) X-ray images from healthy patients
3. Train a CNN to automatically detect COVID-19 in X-ray images via the dataset we created
4. Evaluate the results from an educational perspective
-----------------------------------------------------------------------------------------------

In order to create the COVID-19 X-ray image dataset:

1. Parsed the metadata.csv file found in Dr. Cohen’s repository.
2. Selected all rows that are:
a) Positive for COVID-19 (i.e., ignoring MERS, SARS, and ARDS cases).
b) Posterioranterior (PA) view of the lungs. I used the PA view as, to my knowledge, that was the view used for my “healthy” cases, as discussed below; however, I’m sure that a medical professional will be able clarify and correct me if I am incorrect (which I very well may be, this is just an example)


-------------------------------------------------------------------------

Our three command line arguments:

1) --dataset: The path to our input dataset of chest X-ray images.
2) --plot: An optional path to an output training history plot. By default the plot is named plot.png unless otherwise specified via the command line.
3) --model: The optional path to our output COVID-19 model; by default it will be named covid19.model.
---------------------------------------------------------------------------------------------

import the necessary packages
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import cv2
import os


#COVID19#DeepLearningTutorial#VGG16
-----------------------------------------------------------------------------------------------------

PREPARE THE DATASET:
COVID-19 IMAGES OF CHEST X-RAY : https://github.com/ieee8023/covid-che...
NORMAL IMAGES OF CHEST X-RAY: https://www.kaggle.com/paultimothymoo...
------------------------------------------------------------------------------------------------------

RESOURCES:

1) https://www.pyimagesearch.com/
2) https://docs.google.com/presentation/...