ArticleJournal of cancer research and clinical oncology2023
Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Impact of CT Intensity and Contrast Variability on Deep-Learning-Based Lung-Nodule Detection: A Systematic Review of Preprocessing and Harmonization Strategies (2020-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing lung cancer detection through integrated deep learning and transformer models.Scientific reports · 2025Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.Journal of imaging · 2024Article
- Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.Cancer medicine · 2024Review
- Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.Heliyon · 2023Article
- Genetically Optimized Modular Neural Networks for Precision Lung Cancer Diagnosis: Exploratory Study of Novel Approach.Cancer diagnosis & prognosisArticle
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.
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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.