Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier

TL;DR

This paper introduces Self-DenseMobileNet, a robust lung nodule classification framework that combines advanced image processing, ensemble machine learning, and interpretability techniques, achieving high accuracy and strong generalizability.

Contribution

The study presents a novel framework integrating Self-DenseMobileNet with stacking-based meta-classification and CAM visualization for improved lung nodule detection.

Findings

Achieved 99.28% accuracy on internal data

Maintained 89.40% accuracy on external validation

Enhanced interpretability with CAM visualization

Abstract

In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our approach integrates advanced image standardization and enhancement techniques to optimize the input quality, thereby improving classification accuracy. To enhance predictive accuracy and leverage the strengths of multiple models, the prediction probabilities from Self-DenseMobileNet were transformed into tabular data and used to train eight classical machine learning (ML) models; the top three performers were then combined via a stacking algorithm, creating a robust meta-classifier that integrates their collective insights for superior classification performance. To enhance the interpretability of our results, we employed class activation mapping (CAM) to visualize the decision-making process of the best-performing model. Our proposed framework demonstrated remarkable performance on internal validation data, achieving an accuracy of 99.28\% using a Meta-Random Forest Classifier. When tested on an external dataset, the framework maintained strong generalizability with an accuracy of 89.40\%. These results highlight a significant improvement in the classification of CXRs with lung nodules.