Unleashing the Potential of Artificial Intelligence and Deep Learning in Pneumonia Detection Systems

Bacteria, viruses, and fungi are all examples of microorganisms that have the potential to infect the lungs and cause pneumonia. This infection causes the alveoli of the lungs to become inflamed, which makes it difficult for people to breathe. The severity of the condition ranges from mild to severe. Blood tests, pulse oximetry, and chest X-rays are three of the diagnostic procedures that are utilized. Artificial intelligence (AI) and deep learning have been utilized for image analysis in a variety of medical sectors throughout the course of the past few years. They are pretty helpful when it comes to extracting complicated information from the photographs that are loaded into the system. When it comes to the detection and classification of lung disorders, deep learning is very necessary. This research provides a comprehensive analysis of the medical image pneumonia identification strategies that are utilized in deep learning. The goal of this study is to introduce a novel approach called Artificial Intelligence based Learning for Pneumonia Detection (AILPD), in which it is used to explain its implementation in a system for detecting the pneumonia illness, to present a review of recent advancements in the field, and to provide ideas for future possibilities along similar lines. The purpose of this research is to evaluate the effectiveness of the proposed technique by contrasting the proposed algorithm with the traditional Convolutional Neural Network (CNN) approach. Additionally, this research offers a full review of the deep learning technologies that are utilized in the prevention and treatment of pneumonia.