Advanced Deep Learning Integration for Early Pneumonia Detection for Smart Healthcare

Abstract

The surveillance of symptoms related to pneumonia is increasingly crucial due to its widespread occurrence and similarity to symptoms exhibited in other contagious diseases such as influenza, respiratory syncytial virus (RSV), and COVID-19. The timely identification of pneumonia can significantly diminish mortality rates. To tackle this issue, a pioneering non-contact technique for monitoring pneumonia symptoms has been developed. This investigation mainly concentrates on the early detection of pneumonia, which can serve as an indicator of the onset of other persistent ailments. The technique entails an analytical approach to scrutinize symptoms such as cold, cough, chills, sore throat, altered respiratory rates, and elevated body temperature by employing depth imaging methods. The crux of this exploration lies in the utilization of a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify video images in order to identify symptoms associated with pneumonia. The proposed model has showcased an impressive overall accuracy rate of 98.02% along with a significantly optimized prediction time of a mere 8.63 ms. Moreover, the study encompasses a comprehensive evaluation of various deep learning techniques in the detection of diseases exhibiting symptoms akin to pneumonia. The study introduces a pivotal advancement in medical diagnostics, emphasizing the importance and effectiveness of a fusion-based, profound learning system in the non-contact identification of pneumonia symptoms. This innovative approach has the potential to revolutionize the way pneumonia and similar diseases are diagnosed and monitored.