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https://github.com/divakarkumarp/pneumonia-detection

Deep learning (DL) model is a pneumonia fighter! Trained on chest X-ray images, it analyzes patterns to detect the lung infection. Imagine a digital doctor scrutinizing the X-ray, pinpointing areas that might be pneumonia. The model outputs a probability score, helping doctors confirm or rule out the illness.
https://github.com/divakarkumarp/pneumonia-detection

cnn deep-learning docker keras python scikit-learn streamlit tensorflow

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Deep learning (DL) model is a pneumonia fighter! Trained on chest X-ray images, it analyzes patterns to detect the lung infection. Imagine a digital doctor scrutinizing the X-ray, pinpointing areas that might be pneumonia. The model outputs a probability score, helping doctors confirm or rule out the illness.

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README 

        
# Pneumonia-Detection

![alt text](image.png)



Image. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6

The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs.



Pneumonia is an inflammatory condition of the lung affecting primarily the small air sacs known as alveoli.Symptoms typically include some combination of productive or dry cough, chest pain, fever and difficulty breathing. The severity of the condition is variable. Pneumonia is usually caused by infection with viruses or bacteria and less commonly by other microorganisms, certain medications or conditions such as autoimmune diseases.Risk factors include cystic fibrosis, chronic obstructive pulmonary disease (COPD), asthma, diabetes, heart failure, a history of smoking, a poor ability to cough such as following a stroke and a weak immune system. Diagnosis is often based on symptoms and physical examination. Chest X-ray, blood tests, and culture of the sputum may help confirm the diagnosis.The disease may be classified by where it was acquired, such as community- or hospital-acquired or healthcare-associated pneumonia.



![image](https://github.com/divakarkumar424/Boston-House-Prices-Prediction/assets/32620288/81634454-7f52-4a2a-a8c8-2ec91e465dc9)



## Content

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).



Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.



For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.



URL : https://detectionpneumonia.streamlit.app/



![image](https://github.com/divakarkumar424/Boston-House-Prices-Prediction/assets/32620288/2f964bff-ed08-4229-83ff-9a822f705d5e)



## Overview:

Software And Tools Requirements



1. [Github Account](https://github.com)

2. [Streamlit](https://streamlit.io/)

3. [VSCodeIDE](https://code.visualstudio.com/)

4. [GitCLI](https://git-scm.com/book/en/v2/Getting-Started-The-Command-Line)



Technology and tools wise this project covers,



1. Python

2. Numpy and Pandas for data cleaning

3. Data visualization

4. Sklearn for model building

5. Deep learning (CNN)

6. Jupyter Notebook

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### Technologies Used:



![](https://forthebadge.com/images/badges/made-with-python.svg)



[](https://numpy.org)    [](https://pandas.pydata.org)  [](https://seaborn.pydata.org) [](https://matplotlib.org)   [](https://jupyter.org/)

[](https://scikit-learn.org/stable/index.html)

[](https://streamlit.io/)

[](https://www.docker.com/)