ArticleJournal of digital imaging2019
Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.Scientific reports · 2026Article
- Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.BMC pulmonary medicine · 2025Article
- Multi-scale detection of pulmonary nodules by integrating attention mechanism.Scientific reports · 2023Article
- Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.La Radiologia medica · 2023Article
- Lung Nodule Detection via Optimized Convolutional Neural Network: Impact of Improved Moth Flame Algorithm.Sensing and imaging · 2023Article
- Practice toward standardized performance testing of computer-aided detection algorithms for pulmonary nodule.Frontiers in public health · 2022Article
- A holistic overview of deep learning approach in medical imaging.Multimedia systems · 2022Article
- State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods.Cancers · 2021Review
- CSGBBNet: An Explainable Deep Learning Framework for COVID-19 Detection.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
5 authors.
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Abstract
Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To solve the similar problem of diagnostic lung nodule detection in low-dose computed tomography (CT) scans, we propose a new Computer-Aided Detection (CAD) system using CNNs and CT image segmentation techniques. Unlike former studies focusing on the classification of malignant nodule types or relying on prior image processing, in this work, we put raw CT image patches directly in CNNs to reduce the complexity of the system. Specifically, we split each CT image into several patches, which are divided into 6 types consisting of 3 nodule types and 3 non-nodule types. We compare the performance of ResNet with different CNNs architectures on CT images from a publicly available dataset named the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). Results show that our best model reaches a high detection sensitivity of 92.8% with 8 false positives per scan (FPs/scan). Compared with related work, our work obtains a state-of-the-art effect.
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