ArticleScientific reports2024
Effective lung nodule detection using deep CNN with dual attention mechanisms.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 74 citations in OpenAlex.
- Micro-CT radiomics reveals the microcalcification-surrounding tissue interface as the diagnostic epicenter of breast microcalcifications.Breast cancer research : BCR · 2026Article
- Hybrid Curriculum Learning for Data-Efficient Lung Nodule Detection with YOLOv11.Diagnostics (Basel, Switzerland) · 2026Article
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.Bioengineering (Basel, Switzerland) · 2026Article
- EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images.Journal of imaging informatics in medicine · 2026Article
- Lightweight Visual Dynamic Gesture Recognition System Based on CNN-LSTM-DSA.Sensors (Basel, Switzerland) · 2026Article
- A panel of machine learning approaches for diagnostic model development and validation in ovarian cancer.Translational cancer research · 2026Article
- Impact of CT Intensity and Contrast Variability on Deep-Learning-Based Lung-Nodule Detection: A Systematic Review of Preprocessing and Harmonization Strategies (2020-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence Assisted Smartphone System for Real-Time Detection and Severity Assessment of Digital Clubbing.International journal of medical sciences · 2026Article
- Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.International journal of biomedical imaging · 2026Article
- CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation.Frontiers in artificial intelligence · 2026Article
- Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures.Frontiers in big data · 2026Article
- GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation.NPJ digital medicine · 2025Article
- Multi-phase deep learning framework with Multiscale Adaptive Swin Transformer and embedding attention for precision lung nodule detection and classification.Scientific reports · 2025Article
- A novel method based on a multiscale convolution neural network for identifying lung nodules.Scientific reports · 2025Article
- Automated pulmonary nodule classification from low-dose CT images using ERBNet: an ensemble learning approach.Medical & biological engineering & computing · 2025Article
- Advancements in noise reduction for wheel speed sensing using enhanced LSTM models.Scientific reports · 2025Article
- FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.Scientific reports · 2025Article
- Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.Scientific reports · 2025Article
- Cnidaria herd optimized fuzzy C-means clustering enabled deep learning model for lung nodule detection.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.