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

arXiv: https://arxiv.org/abs/2410.12584 · HTML: https://mlsanity.com/paper/2410.12584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 78 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [COVID-19 Infection Map Generation and Detection from Chest X-Ray Images](https://mlsanity.com/paper/2009.12698/) (2020)
- [CVNets: High Performance Library for Computer Vision](https://mlsanity.com/paper/2206.02002/) (2022)
- [Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach](https://mlsanity.com/paper/1808.10858/) (2018)
- [Nodule detection and generation on chest X-rays: NODE21 Challenge](https://mlsanity.com/paper/2401.02192/) (2024)
- [Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images](https://mlsanity.com/paper/2006.02570/) (2020)
- [Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale Dataset](https://mlsanity.com/paper/1906.00768/) (2019)
- [RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection](https://mlsanity.com/paper/1805.11856/) (2018)

## Built on by (most cited first, 2 of 2)

- [CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram](https://mlsanity.com/paper/2510.27315/) (2025)
- [DeepAgent: A Dual Stream Multi Agent Fusion for Robust Multimodal Deepfake Detection](https://mlsanity.com/paper/2512.07351/) (2025)

## Abstract (arXiv)

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.