Automatic detection of tb using soft computing techniques

This study presents a novel approach for the automatic detection of tuberculosis (TB) using soft computing techniques, including artificial intelligence and machine learning. By leveraging medical imaging data such as chest X-rays, the method enhances traditional diagnostic methods through image preprocessing, feature extraction, and advanced classification algorithms like neural networks, fuzzy logic, and support vector machines. A labeled dataset is used to train and validate the models, significantly improving detection accuracy compared to conventional methods