This project uses Deep learning concept in detection of Various Deadly diseases. It can Detect 1) Lung Cancer 2) Covid-19 3)Tuberculosis 4) Pneumonia. It uses CT-Scan and X-ray Images of chest/lung in detecting the disease. It has a Accuracy between 50%-80%. It can take input in any Image format or through Live videos and provide accurate output…

Lung segmentation for chest X-Ray images with ResUNet and UNet. In addition, feature extraction and tuberculosis cases diagnosis had developed.

Deep Learning Model with CNN to detect whether a person is having pneumonia or tuberculosis based on the chest x-ray images

Deep learning for interpreting chest x-rays

This project uses deep learning algorithms and the Keras library to determine if a person has certain diseases or not from their chest x-rays and other scans. The trained model is displayed using Streamlit, which enables the user to upload an image and receive instant feedback.

Here I have created a convolution deep neural network architecture that correctly identifies tuberculosis infected chest x-ray with an impressive accuracy of 90 percent.

I created various dashboards to ascertain (a)Prevalence of all forms of TB across various countries divided into 6 regions, (b)Distribution of mortality, (c) Evaluation of Mortality (d)Comorbidities with HIV

This project is a tool to build CheXNet-like models, written in Keras for TB X-ray detection

A CNN model that can classify X-Ray images as a Tuberculosis case or a Normal case.

SDAIA's AI Bootcamp project. Tuberculosis detection.

A machine learning model that classifies whether or not a person has Tuberculosis based on their X-Ray