This project is made as a part of Capgemini AI/ML training by Rishika Garg(CS) and Harsh Shukla(IT)

Chest X-ray imaging is the most frequently used method for diagnosing pneumonia. However, the examination of chest X-rays is a challenging task and is prone to subjective variability. The dataset used is avaiable on Kaggle and consists of 5863 images divided into two classes. 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.

You can use your system installed Python interpreter or IDE, or Google Colab to run this model. It was originally trained on the Colab Notebook, due to its availability of high speed GPUs

• Download the kaggle dataset
• Use the code given in the GitHub Repo

This figure illustrates examples of chest X-rays in patients with pneumonia.

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

Please note that the model does not differentiate between viral and bacterial pneumonia

• Test data set: https://drive.google.com/drive/folders/1Y5Jfg5xFFnIL8gtFjgkIyYpUY-x-nt1W?usp=sharing
• Training data set : https://drive.google.com/drive/folders/1n6NtYDuDoU93-FANXoBsMnhc0V5xqDLq?usp=sharing
• kaggle link : https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia