ArticleInternational journal of computer assisted radiology and surgery2021
A bilinear convolutional neural network for lung nodules classification on CT images.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung cancer risk prediction models based on pulmonary nodules: A systematic review.Thoracic cancer · 2022Pooled it
- Enhanced Superpixel-Guided ResNet Framework with Optimized Deep-Weighted Averaging-Based Feature Fusion for Lung Cancer Detection in Histopathological Images.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.Quantitative imaging in medicine and surgery · 2023Article
- Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.Journal of thoracic disease · 2023Article
- MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.International journal of computer assisted radiology and surgery · 2023Article
- Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning.International journal of biomedical imaging · 2023Article
- Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification.Sensors (Basel, Switzerland) · 2022Article
- A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided byJournal of Zhejiang University. Science. B · 2022Article
- Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future.Diagnostics (Basel, Switzerland) · 2022Review
- Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO.Scientific reports · 2022Article
- Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images.Biomedical signal processing and control · 2022Article
- Machine Learning for Early Parkinson's Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features.Journal of imaging · 2022Article
- Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- DAFLNet: Dual Asymmetric Feature Learning Network for COVID-19 Disease Diagnosis in X-Rays.Computational and mathematical methods in medicine · 2022Article
- A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules.Journal of thoracic disease · 2021Article
- Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM.Computational intelligence and neuroscience · 2021Article
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Authors and funding
4 authors.
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Abstract
purposeLung cancer is the most frequent cancer worldwide and is the leading cause of cancer-related deaths. Its early detection and treatment at the stage of a lung nodule improve the prognosis. In this study was proposed a new classification approach named bilinear convolutional neural network (BCNN) for the classification of lung nodules on CT images.
methodsConvolutional neural network (CNN) is considered as the leading model in deep learning and is highly recommended for the design of computer-aided diagnosis systems thanks to its promising results on medical image analysis. The proposed BCNN scheme consists of two-stream CNNs (VGG16 and VGG19) as feature extractors followed by a support vector machine (SVM) classifier for false positive reduction. Series of experiments are performed by introducing the bilinear vector features extracted from three BCNN combinations into various types of SVMs that we adopted instead of the original softmax to determine the most suitable classifier for our study.
resultsThe method performance was evaluated on 3186 images from the public LUNA16 database. We found that the BCNN [VGG16, VGG19] combination with and without SVM surpassed the [VGG16]2 and [VGG19]2 architectures, achieved an accuracy rate of 91.99% against 91.84% and 90.58%, respectively, and an area under the curve (AUC) rate of 95.9% against 94.8% and 94%, respectively.
conclusionThe proposed method improved the outcomes of conventional CNN-based architectures and showed promising and satisfying results, compared to other works, with an affordable complexity. We believe that the proposed BCNN can be used as an assessment tool for radiologists to make a precise analysis of lung nodules and an early diagnosis of lung cancers.
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