Deep Learning Module for Spectroscopic Identification of Pathogenic Bacteria

Abstract

Bacterial infections are a major threat to the health-care sector and a leading cause of death in many countries. According to the study, over 15 million antibiotic-resistant infections occur in the India each year, resulting in over 170,000 deaths. To overcome the problem an intelligent techniques like computer vision in a single stage, it promises label-free bacterial detection, recognition, and antibiotic susceptibility testing. Traditional sample clustering is the latest diagnostic approach which is used to detect and identify the bacteria and its antibiotic susceptibility. This procedure is leisure process and it takes around seven days even in acknowledged laboratories. This led to wide range of generic antibiotics in prescription while patient is still waiting for the result. Utilization of antibiotics may cause side effects such as anti-microbial resistance, drug infections, digestive problems, fungal infections. Intelligent technique for rapid, culture free diagnosis of bacterial infection is enabling earlier prescription of target antibiotics and helps mitigate antimicrobial resistance.