ArticleMedical & biological engineering & computing2022
CAD system for lung nodule detection using deep learning with CNN.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.Statistics in medicine · 2026Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- VascuConNet: an enhanced connectivity network for vascular segmentation.Medical & biological engineering & computing · 2024Article
- Application value of a computer-aided diagnosis and management system for the detection of lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmonary nodules in computed tomography (CT) images. Our proposed CNN-5CL (convolutional neural network with 5 convolutional layers) approach uses an 11-layer convolutional neural network (with 5 convolutional layers) for automatic feature extraction and classification. The proposed method is evaluated using LIDC/IDRI images. The proposed method is implemented in the Python platform, and the performance is evaluated with metrics such as accuracy, sensitivity, specificity, and receiver operating characteristics (ROC). The results show that the proposed method achieves accuracy, sensitivity, specificity, and area under the roc curve (AUC) of 98.88%, 99.62%, 93.73%, and 0.928, respectively. The proposed approach outperforms various other methods such as Naïve Bayes, K-nearest neighbor, support vector machine, adaptive neuro fuzzy inference system methods, and also other deep learning-based approaches.
Indexed as
Identifiers
34811644What OpenQuestion holds
Registered trials
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.