Publication

Abstract : The healthcare sector has observed notable progressions as a result of incorporating computer technology and artificial intelligence (AI). This study explores the application of deep learning and machine learning techniques to the detection and management of pneumonia, a common respiratory disease caused by bacteria, viruses, or fungi. Pneumonia poses a significant hurdle in medical environments due to its diverse symptoms and the necessity for accurate diagnosis to ensure effective therapy. This study uses artificial intelligence (AI), namely convolutional neural networks (CNN), VGG16, ResNet50v2, and the most recent YOLOv8 algorithm, to increase the accuracy and efficiency of pneumonia recognition from chest X-ray images. Our dataset, obtained from Guangzhou Women and Children’s Medical Center, consists of 5,866 X-ray images that have carefully been examined and annotated to uphold data integrity. The investigation offers a Comparative evaluation of the performance of different AI models. This is in categorizing pneumonia as opposed to normal instances. The YOLOv8 model emerged as the leading performer, attaining an impressive mean average precision (mAP) score of 97.4%. demonstrating its potential for instantaneous, high-accuracy Diagnostic utilities: The results highlight the transformative influence of AI in clinical decision support systems (CDSS). streamlining prompt and accurate diagnoses that can substantially enhance patient outcomes. This manuscript aims to provide perspectives on the implementation of advanced AI models in healthcare, accentuating their capacity to revolutionize Pneumonia diagnosis and treatment