Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
Advanced diagnostic instruments are crucial for the accurate detection and
treatment of lung diseases, which affect millions of individuals globally. This
study examines the effectiveness of deep learning and transfer learning models
using a hybrid dataset, created by merging four individual datasets from
Bangladesh and global sources. The hybrid dataset significantly enhances model
accuracy and generalizability, particularly in detecting COVID-19, pneumonia,
lung opacity, and normal lung conditions from chest X-ray images. A range of
models, including CNN, VGG16, VGG19, InceptionV3, Xception, ResNet50V2,
InceptionResNetV2, MobileNetV2, and DenseNet121, were applied to both
individual and hybrid datasets. The results showed superior performance on the
hybrid dataset, with VGG16, Xception, ResNet50V2, and DenseNet121 each
achieving an accuracy of 99%. This consistent performance across the hybrid
dataset highlights the robustness of these models in handling diverse data
while maintaining high accuracy. To understand the models implicit behavior,
explainable AI techniques were employed to illuminate their black-box nature.
Specifically, LIME was used to enhance the interpretability of model
predictions, especially in cases of misclassification, contributing to the
development of reliable and interpretable AI-driven solutions for medical
imaging.
