HybridNoduleNet: Noise-Resilient Lung Nodule Classification Using CNN–ViT–DAE Architecture with Grad-CAM Interpretability
Received: 25 January 2026 | Revised: 2 March 2026 and 20 March 2026 | Accepted: 21 March 2026 | Online: 11 April 2026
Corresponding author: Hasan Hussain Shahul Hameed
Abstract
Lung nodule classification from Computed Tomography (CT) images is challenged by acquisition noise, anatomical variability, and the need for reliable model interpretability in clinical practice. The latest deep learning models achieve better classification results, but their performance drops when handling noisy data, and their explanation methods lose reliability because denoising and interpretability functions operate independently of their core operations. The research presents HybridNoduleNet as a complete noise-resistant system, which combines denoising and feature extraction with adaptive fusion and interpretability within a single learning framework. The framework sequentially performs noise modeling and denoising reconstruction, parallel Convolutional Neural Network (CNN) and Vision Transformer (ViT) feature extraction, adaptive cross-attention fusion, and Gradient-weighted Class Activation Mapping (Grad-CAM)-guided classification within a unified learning pipeline. The Denoising Autoencoder (DAE) learns noise-invariant representations through joint optimization with the classifier. It processes local and global features by running parallel CNN and ViT streams. The cross-attention fusion mechanism automatically adjusts these representations, and Grad-CAM produces spatially consistent explanations. The proposed framework is evaluated on the LUNA16 and LIDC-IDRI datasets under clean conditions as well as Gaussian and Poisson noise perturbations. Experimental results show that HybridNoduleNet consistently outperforms convolutional, transformer-based, and existing hybrid baselines, achieving classification accuracy above 92% under severe noise conditions. Improvements of up to 5.4% in recall and over 10% in noise robustness are observed, along with more stable and localized Grad-CAM activations, yielding Intersection over Union (IoU) gains exceeding 0.30 in noisy settings. These findings demonstrate that HybridNoduleNet provides a robust and interpretable solution for noise-aware lung nodule classification in low-dose CT imaging.
Keywords:
lung nodule classification, Denoising Autoencoder (DAE), CNN–ViT hybrid architecture, noise robustness, Grad-CAM interpretability, medical image analysisDownloads
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Copyright (c) 2026 M. R. Venkatesh, Hasan Hussain Shahul Hameed
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