SEDLF‐LDD: A Stacking Ensemble‐Based Deep Learning Framework for Lung Disease Diagnosis

ABSTRACT There is a growing need for accurate and swift diagnostic tools for lung disease diagnosis in healthcare. This work presents a Stacking Ensemble‐based Deep Learning Framework for Enhanced Lung Disease Diagnosis (SEDLF‐LDD). The stacking is a widely used ensemble learning technique that enhances the model's performance by combining the predictions from multiple base‐learners using a meta‐learner. The proposed framework selects the five best‐performing pre‐trained models, namely, ResNet50, MobileNetV2, VGG16, VGG19, and DenseNet201, as the base‐learners and Multilayer Perceptron (MLP) as a meta‐learner. To ensure broader applicability, we curated a dataset of chest X‐ray images of Lung Disease. Initially, we choose the ten transfer learning models, fine‐tune them to extract features relevant to respiratory diseases on the dataset, and select Top‐5 best‐performing models as base‐learners. The effectiveness of the framework is determined by analysis of precision, recall, F1‐score, or the area under the receiver operator characteristic (AUC‐ROC) curve. The experimental results show an effective result with 97.65% accuracy.