ReviewJournal of imaging2020
A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions.
Review in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening.Journal of healthcare engineering · 2022Trial
- Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs.Neonatology · 2026Article
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- A Two-Stage Contrastive Learning Framework Grounded in Label-Specific Features for Low-Frequency Labels in Chest X-Ray Multi-Label Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Non-contact lung disease classification via orthogonal frequency division multiplexing-based passive 6G integrated sensing and communication.Communications medicine · 2026Article
- Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine.World journal of critical care medicine · 2025Review
- Deep learning approach for automated hMPV classification.Scientific reports · 2025Article
- Treatment of lung diseasesMaterials today. Bio · 2025Review
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.Scientific reports · 2025Article
- CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images.Bioengineering (Basel, Switzerland) · 2025Article
- Evaluation of Deep Learning Methods for Pulmonary Disease Classification.Current medical imaging · 2025Article
- Cross-domain lung opacity detection via adversarial learning and box fusion.Scientific reports · 2024Article
- Worldwide research landscape of artificial intelligence in lung disease: A scientometric study.Heliyon · 2024Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Design of Interoperable Electronic Health Record (EHR) Application for Early Detection of Lung Diseases Using a Decision Support System by Expanding Deep Learning Techniques.The open respiratory medicine journal · 2024Article
- Paddynet: An organized dataset of paddy leaves for a smart fertilizer recommendation system.Data in brief · 2023Article
- Article
- Vision Transformers for Lung Segmentation on CXR Images.SN computer science · 2023Article
- A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images.Cluster computing · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.
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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.