Identification of Tuberculosis Bacilli from Bright Field Microscopic Sputum Smear Images using U-Net and Random Forest Classification Algorithm

Abstract

Tuberculosis (TB) is an infectious illness that may be severe and primarily impacts the lungs. Examining sputum smears under bright field microscopes is one of the simplest and most successful ways to detect TB infection in impoverished nations like India. A method for detecting tuberculosis bacteria from bright-field microscopic sputum smear images is proposed in this work. U-shaped encoder-decoder network architecture (U-Net) is used to first segment the bright field microscopic sputum smear images, and then Random Forest Classification Algorithm is used for final prediction. The detection of bacilli produced results that are comparable to other methods.