Analysis of Deep Learning Techniques for Tuberculosis Disease

Abstract

The airborne disease is a severe disease in the world that spreads exponentially. An Immunochromatography Test(IT) and Biological Aerosol Particles (BAP) test with a disposable instrument that will be recorded in the literature is a standard diagnostic procedure for respiratory infections, such as influenza and TB, in the investigation causes of suspected disease. This examination helps the examiner to identify infectious patients quickly, efficiently, and inexpensively. Self-diagnosis, however, is problematic because it requires a medical specialist to diagnose the diseases in infections. Chest radiography is also not only the primary screening method that is focused on surgical imaging but also diagnostic radiology. Various methods of classification of Tuberculosis are analyzed here. Computer-Assisted Diagnosis (CAD) has become popular, and many researchers are involved in these fields of study. Numerous methods have been suggested for airborne identification and Classification of lung diseases. The medical history of airborne tuberculosis disease in chest X-rays and a review of the different approaches to diagnosing and Classification of the airborne disease are discussed in this article. According to the literature on the relevant policies, items in this research field have been surveyed until 2020.

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

“…In addition, deep learning can develop models that can accurately predict and diagnose illnesses using images. It has been effective in diagnosing TB [5] , [6] , [7] , [8] , [9] , [10] , pneumonia [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , lung cancer [19] , [20] , [21] , [22] , [23] , and COVID-19 diagnosis, without need of human expertise. Unlike traditional machine learning, the fundamental reason behind using deep learning techniques is its ability to build the model of input as the size of network deeply grows.…”

Fuzzy ELECTRE and TOPSIS method to analyze the risk factors of tuberculosis

“…Web GIS-based valuation using SAW methods was designed to identify high risk [2]. Deep learning techniques were used to identify tuberculosis disease [13]. A fuzzy expert system was designed for the diagnosis of tuberculosis [14].…”