ArticleInternational journal of biomedical imaging2013
Computer-aided diagnosis systems for lung cancer: challenges and methodologies.
Article in International journal of biomedical imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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The trial behind it
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Who cites it
50 citing papers in PubMed, 276 citations in OpenAlex.
- Results of the two incidence screenings in the National Lung Screening Trial.The New England journal of medicine · 2013Trial
- A Symmetric Encoder-Decoder Network with Enhanced Group-Shuffle Modules for Robust Lung Nodule Detection in CT Scans.Biomimetics (Basel, Switzerland) · 2026Article
- LungCraft: a hybrid 3D-2D deep learning and radiomics framework with explainable AI for precision diagnosis of lung cancer.Frontiers in artificial intelligence · 2026Article
- Few-Shot Learning for CT Lung Nodule Detection Based on Open-Set Object Detection.Current medical science · 2025Article
- Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025Review
- The Solid Volume Ratio is Better Than the Consolidation Tumor Ratio in Predicting the Malignant Pathological Features of cT1 Lung Adenocarcinoma.The Thoracic and cardiovascular surgeon · 2025Article
- Explainable AI for lung cancer detection via a custom CNN on CT images.Scientific reports · 2025Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Deep Learning-Based Slice Thickness Reduction for Computer-Aided Detection of Lung Nodules in Thick-Slice CT.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review.Life (Basel, Switzerland) · 2023Review
- Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.Healthcare (Basel, Switzerland) · 2023Review
- Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.Journal of clinical medicine · 2022Article
- Classification of breast cancer histology images using MSMV-PFENet.Scientific reports · 2022Article
- Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2022Review
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2022Article
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.
Identifiers
What 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.