ArticleSensors (Basel, Switzerland)2020
Multi-Level Cross Residual Network for Lung Nodule Classification.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 53 citations in OpenAlex.
- AI-Driven Tumor Characterization and Histological Subtype Classification in Lung Cancer Using CT Imaging.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing lung cancer classification through a double attention hybrid CNN-HiFuse approach.Scientific reports · 2026Article
- A novel two-stage deep learning approach for lung cancer using enhanced ResNet50 segmentation and LungSwarmNet classification.Scientific reports · 2025Article
- Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.PloS one · 2025Article
- A Novel Automatic Lung Nodule Classification Scheme using Fusion Ghost Convolution and Hybrid Normalization in Chest CTsCurrent medical imaging · 2025Article
- Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities.Diagnostics (Basel, Switzerland) · 2024Article
- Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature.BMC medical imaging · 2024Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- Article
- Single Modality vs. Multimodality: What Works Best for Lung Cancer Screening?Sensors (Basel, Switzerland) · 2023Article
- A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.Frontiers in oncology · 2023Article
- A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided byJournal of Zhejiang University. Science. B · 2022Article
- Non-small cell lung cancer in China.Cancer communications (London, England) · 2022Review
- A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network.Journal of oncology · 2022Article
- Multi-Dimension and Multi-Feature Hybrid Learning Network for Classifying the Sub Pathological Type of Lung Nodules through LDCT.Sensors (Basel, Switzerland) · 2021Article
- LdsConv: Learned Depthwise Separable Convolutions by Group Pruning.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.
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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.