Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determine the severity of infections. Different countries face unique challenges related to these diseases. This study introduces innovative Artificial Intelligence (AI) based methodologies to enhance diagnostic accuracy through the analysis of medical imagery. It achieves this by developing a mathematical model capable of identifying potential infectious diseases from images, utilizing a Multi-Criteria Decision-Making (MCDM) framework. This cutting-edge approach combines Hypersoft Set (HSS) within a fuzzy context, pioneering in AI-driven diagnostic processes. The decision-making process might suggest actions such as isolation, quarantine in either domestic settings or specialized facilities, or admission to a hospital for further treatment. The use of visual aids in this research not only improves understanding but also highlights the effectiveness and significance of the proposed methods. The foundational theory and the results from this novel approach demonstrate its potential for widespread application in fields like machine learning, deep learning, and pattern recognition, indicating a significant stride in the fight against infectious diseases through advanced diagnostic techniques.