Pneumonia can prevent your lungs from moving enough oxygen into your bloodstream. A sample of fluid from your lungs (sputum) is taken after a deep cough and analyzed to help pinpoint the cause of the infection.
Your doctor might order additional tests if you're older than age 65, are in the hospital, or have serious symptoms or health conditions. If your pneumonia isn't clearing as quickly as expected, your doctor may recommend a chest CT scan to obtain a more detailed image of your lungs.
A fluid sample is taken by putting a needle between your ribs from the pleural area and analyzed to help determine the type of infection. Treatment for pneumonia involves curing the infection and preventing complications.
People who have community-acquired pneumonia usually can be treated at home with medication. Although most symptoms ease in a few days or weeks, the feeling of tiredness can persist for a month or more.
Specific treatments depend on the type and severity of your pneumonia, your age and your overall health. It may take time to identify the type of bacteria causing your pneumonia and to choose the best antibiotic to treat it.
If your symptoms don't improve, your doctor may recommend a different antibiotic. Because coughing helps loosen and move fluid from your lungs, it's a good idea not to eliminate your cough completely.
If you want to try a cough suppressant, use the lowest dose that helps you rest. You may take these as needed for fever and discomfort.
Don't go back to school or work until after your temperature returns to normal, and you stop coughing up mucus. Because pneumonia can recur, it's better not to jump back into your routine until you are fully recovered.
Drink plenty of fluids, especially water, to help loosen mucus in your lungs. Take the entire course of any medications your doctor prescribed for you.
If you stop taking medication too soon, your lungs may continue to harbor bacteria that can multiply and cause your pneumonia to recur. You may start by seeing a primary care doctor or an emergency care doctor, or you may be referred to a doctor who specializes in infectious diseases or in lung disease (pulmonologist).
Here's some information to help you get ready for your appointment and know what to expect. Write down key personnel information, including exposure to any chemicals or toxins, or any recent travel.
Make a list of all medications, vitamins and supplements that you're taking, especially an antibiotic left over from a previous infection, as this can lead to a drug-resistant pneumonia. Bring a family member or friend along, if possible, to help you remember questions to ask and what your doctor said.
Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2014. Schooner S, et al. Community-acquired pneumonia in children: A look at the IDEA guidelines.
Cartridge RT, et al. Health care-associated pneumonia : An evidence-based review. Rockwell DH, et al. Pneumococcal pneumonia : Mechanisms of infection and resolution.
Barbara Woodward Lips Patient Education Center. Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2013.
Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2014. Community-acquired pneumonia in children: Outpatient treatment.
Treatment of community-acquired pneumonia in adults in the outpatient setting. Over-the-counter (OTC) medications to reduce cough as an adjunct to antibiotics for acute pneumonia in children and adults.
CXR will confirm DX:Health Care provider will order a chest ray (CXR) if they want to confirm or rule out Pneumonia. Cold viruses can cause symptoms that seem like PNE ... Read More.
Chest:radiograph is used to check for lung infection. Diagnose pneumonia :Chest radiograph usually taken to confirm pneumonia when patient has fever and cough and some findings on clinical examination that are compatible wit ... Read More.
Not really: pneumonia should show immediately, Earlier some felt it may take time to show in pts with dehydration or neutropenia i.e. white blood cells decreased If you are having symptoms, additional pulmonary testing and imaging may provide a diagnosis.
Detecting Pneumonia from Chest X-Rays with Deep Learning | by Sheila Farmhand | Towards Data Science Photo by CDC on Unsplash Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution.
You should not rely on an author’s works without seeking professional advice. The WHO estimated that 45,000 of these premature deaths were due to household air pollution.
With more efficiency in the diagnostics, many of these deaths can be reduced. The goal of this project is to create various machine learning and deep learning models so that when optimized, can assist radiologists in detecting Pneumonia from Chest X -Rays. NumPy Pandas TensorFlow (Version 1. x) Sci-Kit Learn and Eras Seaborne and Matplotlib.
We will be using other packages to download files and helper functions that do not have to do with building the models. We will also be downloading the data (in this case, images of Chest X -rays) for the project.
After that, we will create a data frame (essentially a table) using the pandas' library, which will give info about the first five images in our dataset. In regard to split, when we are teaching the computer how to do something (in this case diagnose pneumonia), we have to train it, by giving the image with a label.
So, the split column describes whether the image is used to train the computer or test it. We will be using the pandas' method “count” to display the table, and we will be using the seaborne “count plot” to display a graph that classifies the data.
Now that we fully understand which category our data is in, we can go ahead and plot some images, to view what the chest x -rays will look like. It downloads the training and testing data (from the functions we defined earlier) and will plot an image depending on an index (the number associated with the image.
The “data” is the image while the “labels” section is the number 0 or 1 to say whether the patient has pneumonia or not. I specifically chose one healthy image, and one with a lung that has pneumonia.
Now that we finished examining and analyzing our data, we can go ahead and build some machine learning models. We will begin simple, using the K-nearest neighbors, and logistic regression classifiers.
Image by authorship diagram above shows the K-Nearest Neighbors Classifier. For example, if k=1, the (single) the closest shape to the one in question is a circle.
Logistic Regression is used to predict probabilities that will later be turned into a category (0–1). In linear regression, the predicted y-value can exceed the 0–1 range (with the continuous straight line).
However, in logistic regression, the predicted y-values can not exceed the 0–1 range because it is in the shape of an “s”. However, the logistic curve’s y-axis shows the probability of whether they passed the exam.
As you can see, the curve predicts that people who study less will fail, rather than pass, and vice versa. We will be using the sci-kit learn library to build these models, specifically the KNeighborsClassifier and LogisticRegression methods.
Note that you can improve the performance of the CNN classifier by just changing the number of neighbors. A convolutional kernel is a matrix of weights, similar to the ones of a fully-connected layer.
Image by Michael Plot on Public Domain The convolution layer output is still too big for the neural network to make any predictions. The last layer uses an activation function that outputs probabilities, such as soft max or sigmoid, which the computer will use to classify images.
Image by Knell Maine Cause on Public Domain We will be using the Eras library to build our model. Before you build your model, run these two functions, that will allow you to plot accuracy and loss.
Add(Dropout(): Shuts off a certain amount of neurons, to reduce overfitting. Our loss will be binary_crossentropy, and we will use the optimizer, RMS prop, customized to a learning rate of 1e-4 and a decay of 1e-6.
You can from here continue tinkering with the model to increase accuracy and decrease the loss. Whenever there are decimals on the x -axis, you can ignore it, because only full epochs are allowed (whole numbers).
Hopefully, their experience in the Imagine Problem will assist in distinguishing pneumonia from our x -rays. Then we will have to declare it as trainable, making sure we can train every layer, by utilizing a “for” loop.
This is because VGG16 is pre-trained, so that makes it more efficient and accurate, but at the same time, it can overfit easier. Afterwards, we constructed simple machine learning models, that were able to make informed decisions based on the training that we applied to it.
Credit to Inspirit AI for teaching me about all the technical details and providing the dataset. High school student passionate about AI, and computational law.
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