Multimodal Integration of an Enhanced Novel Pulmonary Auscultation Real-Time Diagnostic System

Respiratory illnesses pose a significant threat to life worldwide, necessitating prompt identification and effective intervention. Conventional organ examination methods are restrained by certain shortcomings that lead to inconsistent diagnosis. This research addresses the global threat of respiratory illnesses by introducing a unique hybrid convolutional neural network (CNN)-gated recurrent unit (GRU) architecture deployed on a Raspberry Pi for real-time classification of respiratory auditory cues, leveraging the power of sound in diagnostics. By overcoming the limitations of conventional examination methods, the system was able to achieve an impressive accuracy of 98% in distinguishing unusual auscultations. The system incorporated multimedia elements, particularly sound with CNNs to extract spatial attributes, and GRUs for the comprehension of temporal context. The utilization of an instinctual online interface, complemented by visualizations, dynamic sound patterns, and interactive elements, eased direct communication with medical professionals. The multimedia-centered approach focused particularly on respiratory sound indicates a landmark of respiratory diagnostics that is poised to enhance health-care outcomes globally.