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.

Tuberculosis (TB) remains a significant global health concern, ranking among the top ten causes of mortality worldwide. Timely and accurate detection of TB is pivotal for effective management and containment of the disease. In this study, we developed a robust TB detection system utilizing state-of-the-art methodologies including image preprocessor

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