Tuberculosis Detection using Image Pre-Processing

This paper presents a system and method for identifying tuberculosis (TB) from chest X-ray images. The system includes a number of essential parts such as image acquisition module is in charge of taking pictures of the chest radiography. The input for later processing stages is these image Preprocessing Module Improves contrast and image quality among the methods and contrast enhancement, noise reduction, and histogram equalization which Increases the TB-related features visibility in X-ray pictures. The preprocessed images are used to extract pertinent features by the feature extraction module which makes use of sophisticated image processing techniques. The categorization model uses these features as input for CNN classification model which obtains the features that were extracted and uses learned representations to identify tuberculosis patterns which strives to increase TB diagnosis efficiency and accuracy. The proposed approach holds promise for early and accurate identification of tuberculosis infections, benefiting healthcare professionals and patients in regions with high TB prevalence.