Lung-Ensemble: A Secure Multi-Stage Diagnostic Pipeline for Multi-Class Respiratory Disease Detection using a Stacked Ensemble and Explainable AI

Abstract—Lung diseases continue to be one of the fundamental
health threats for a significant portion of the global population.
Respiratory health and lung function, however, face severe threats
from various infections that significantly increase mortality rates.
In addition, there is a growing need for rapid detection and
accurate diagnosis of these diseases to prevent severe clinical
complications. In this paper, we have explored and analyzed
several state-of-the-art deep learning models for multi-class lung
disease detection using a comprehensive dataset of chest Xrays. From the models we tested, we noticed that MobileNetV2
and VGG16 gave the best individual results. To leverage these
strengths, we propose a secure, multi-stage diagnostic pipeline
called Lung-Ensemble. This framework utilizes a binary ”Gatekeeper” classifier to filter out non-medical images, securing
the system against erroneous processing, before passing valid
X-rays to a feature-fusion based ensemble utilizing VGG16,
DenseNet121, and MobileNetV2. Our proposed model (LungEnsemble) shows a significant improvement, achieving a final
accuracy of 98.33%.