A collection of projects which explore image classification on chest x-ray images (via the NIH dataset)

In this project an Attention Guided CNN was modeled & trained on the chest XRAY dataset provided by NIH & followed the paper: Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L. and Yang, Y. (2019). Thorax Disease Classification with Attention Guided Convolutional Neural Network.

Example implementation of how to use the tf Datasets API by training a model on the NIH Chest Xray data

No description available

Working on the NIH chest xray dataset and Chexpert (stanford chest X-ray dataset) to implement Federated averaging and imrpove accuracy of global model using Densenet121 and Efficientnet standard models

in this repo, the model UnimedCLIP is used as aclassifier on the sample set of NIH chest xray dataset from kaggle. the results are then visualized using sql and google looker studio.

Hospital Management system with 4 types of users and guest: Doctor, technical, receptionist, and Admin and patient the system has one more part which is the computer vision model which deals with X-Ray chest and diagnose them based on NIH chest-Xrays and reported also by the model and allowing to thew doctor to work fast in small time to review these reports

No description available

Use DenseNet121 model to classify x-ray images in Keras

Exploratory repository using convolutional neural networks to classify pathologies in chest x-rays

A conv net for chest xrays, built with TF in Python and R

Stats 426 Project. Applying a variety of CNN on a Chest XRay Dataset

No description available

A professional-grade pipeline for detecting 14 diseases from chest radiographs using the NIH Chest X-ray dataset

Kaggle Challenge: detects pneumonia by x-ray of the patient's lung

NIH CHEST XRay - Kaggle Competition

NIH Chest Xray disease classification using swin transformers

Multi Label Classification for the NIH chest Xray Dataset

AI Project - Detecting Lung Disease using Computer Vision Data Source: https://www.kaggle.com/nih-chest-xrays/data

A combination of 2 ResNets that separate Chest X-Rays from the NIH dataset into normal and abnormal xrays. It prioritises recall to make sure there are fewer false negatives.