Enhanced approach to detect disease in the Respiratory System

Respiratory diseases pose significant health challenges worldwide, necessitating accurate and timely diagnosis for effective management. This paper proposes an approach for automated respiratory condition detection using X-ray images, leveraging ensemble learning techniques that integrate multiple models to enhance classification performance. The system is evaluated on a diverse dataset comprising images representing various respiratory conditions, including pneumonia, tuberculosis, normal lung, and coronavirus. The experiments demonstrate the efficiency of the ensemble model in accurately identifying different respiratory conditions, showcasing particular strengths. Furthermore, this research compares the ensemble model to individual models like VGGNet and ResNet, underscoring the superiority of the ensemble approach and elucidating its distinct advantages over traditional transfer learning models. The results indicate that combining different models through ensemble learning significantly improves diagnostic accuracy, aiding better decision-making and patient care.