ArticleJournal of digital imaging2020
Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.Japanese journal of radiology · 2025Article
- An adaptive and lightweight YOLOv5 detection model for lung tumor in PET/CT images.Scientific reports · 2024Article
- GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.BMC pulmonary medicine · 2024Article
- A semantic fidelity interpretable-assisted decision model for lung nodule classification.International journal of computer assisted radiology and surgery · 2024Article
- A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review.Life (Basel, Switzerland) · 2023Review
- Automatic brain tumor detection using CNN transfer learning approach.Medical & biological engineering & computing · 2023Article
- Multi-Modal Feature Fusion-Based Multi-Branch Classification Network for Pulmonary Nodule Malignancy Suspiciousness Diagnosis.Journal of digital imaging · 2023Article
- [A three dimensional convolutional neural network pulmonary nodule detection algorithm based on the multi-scale attention mechanism].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- CTSC-Net: an effectual CT slice classification network to categorize organ and non-organ slices from a 3-D CT image.Neural computing & applications · 2022Article
- Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.Quantitative imaging in medicine and surgery · 2022Article
- Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.Journal of healthcare engineering · 2021Article
- Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis.Journal of digital imaging · 2020Article
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4 authors.
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Abstract
Lung cancer has the highest mortality rate of all cancers, and early detection can improve survival rates. In the recent years, low-dose CT has been widely used to detect lung cancer. However, the diagnosis is limited by the subjective experience of doctors. Therefore, the main purpose of this study is to use convolutional neural network to realize the benign and malignant classification of pulmonary nodules in CT images. We collected 1004 cases of pulmonary nodules from LIDC-IDRI dataset, among which 554 cases were benign and 450 cases were malignant. According to the doctors' annotates on the center coordinates of the nodules, two 3D CT image patches of pulmonary nodules with different scales were extracted. In this study, our work focuses on two aspects. Firstly, we constructed a multi-stream multi-task network (MSMT), which combined multi-scale feature with multi-attribute classification for the first time, and applied it to the classification of benign and malignant pulmonary nodules. Secondly, we proposed a new loss function to balance the relationship between different attributes. The final experimental results showed that our model was effective compared with the same type of study. The area under ROC curve, accuracy, sensitivity, and specificity were 0.979, 93.92%, 92.60%, and 96.25%, respectively.
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