A Novel Digitized Microscopic Images of ZN-Stained Sputum Smear and Its Classification Based on IUATLD Grades

Abstract

Microscopic detection of acid-fast bacilli (AFB) from mycobacterium tuberculosis (MTB) in Ziehl-Neelsen (ZN)-stained sputum samples is a crucial step in the detection of TB (tuberculosis) disease. Pathologists encounter many challenges that may result in incorrect diagnoses, such as the heterogeneous shape and irregular appearance of MTB, low-quality ZN staining, and errors in in scanning each of the field of view (FoV) using a conventional microscope. Additionally, multiple manual observations may cause fatigue that leads to human error. Several studies have created microscopic imaging databases of sputum samples, aiding researchers in creating computer-aided diagnosis (CAD) for tuberculosis, which is a promising method that offers timely, reliable, and repeatable assistance. Nevertheless, the implementation of CAD systems for TB diagnosis remains an area of ongoing research and development owing to the lack of microscopic image datasets of sputum samples, which represent whole-slide imaging (WSI) that follows the WHO (World Health Organization) regulations. To address this issue, this study developed a novel digitized microscopic image from sputum smear samples of Indonesian patients in the WSI that conform to the WHO regulation. These images are collected as a Microscopic Imaging Database of Tuberculosis Indonesia (MIDTI). This study also proposed a method based on the YOLOv7 (You Only Look Once seventh version) algorithm to develop a CAD for tuberculosis diagnosis by classifying ZN-stained sputum smear samples into International Union Against Tuberculosis and Lung Disease (IUATLD) grades, which has never been revealed in any previous studies.

Keywords

• Tuberculosis
• Ziehl-Neelsen
• acid-fast bacilli
• MIDTI
• IUATLD
• YOLOv7