This project culminates in a model that can classify a given chest x-ray for the presence or absence of pneumonia.

This project uses a deep learning model built with the TensorFlow Library to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model.

Pneumonia detection system using Convolutional Neural Networks (CNNs) on chest X-ray images. The project leverages the Xception pre-trained model and achieves an accuracy of 84.13%.

This repository contains the implementation of a Convolutional Neural Network (CNN) with attention mechanisms for the detection of Pneumonia from chest X-ray images.

Automated Diagnosis of Pneumonia from Classification of Chest X-Ray Images using EfficientNet

This repository includes necessary files, codes required to run Pneumpredict and predict the health of lungs from X-rays.

A spatial pyramid pooling based CNN to classify different types of pneumonia

Creating a Model that can diagnose pneumonia with an xray image

This project uses a pre-trained ResNet50 model from the FastAI library to detect pneumonia in chest X-rays. The dataset which is available on kaggle is used for training the model which classifies the chest xray as NORMAL, VIRAL or BACTERIAL and this project is deployed on Flask

Chest X-Rays Image Classification project from the course Applied AI in Biomedicine @ Politecnico di Milano

Pneumonia classifier GUI with tkinter: based on link below

Train a classifier on the chest x-ray dataset to classify whether an image shows signs of pneumonia or not.

Biomedical Image Analysis FER

Medical Image Processing

This notebook contains the model preparation where the X-Rays are given and we need to classify whether the person's health is Normal or they are suffering from Pneumonia.

Developed and evaluated two models, to detect pneumonia cases from medical images. Our custom resnet18 was evaluated at an 81% accuracy, 66% precision, and 78% recall. Valuable for timely detection of pneumonia patients, improving outcomes, and reducing mortality. CAM visualizations provide provide insights into model decision-making

This project is all about interpreting Chest X-ray images, and the task is to classify whether X-ray image got infected by Pneumonia or not. we also used here GAN and various type of augmentation techniques.

This repository contains a CNN model for pneumonia diagnosis using chest X-ray images. The model achieves 90.54% accuracy on the test dataset.