ArticleThe Visual computer2023
Segmentation and classification on chest radiography: a systematic survey.
Article in The Visual computer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 81 citations in OpenAlex.
- Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.International journal of biomedical imaging · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Conditional diffusion model for high-accuracy brain tumor segmentation in MRI images.Scientific reports · 2025Article
- Segmentation of lungs from chest X-ray images based on Deep Atrous Attention UNet (DAA-UNet) model.Medical & biological engineering & computing · 2025Article
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
- Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis.Sensors (Basel, Switzerland) · 2025Article
- Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.Physical and engineering sciences in medicine · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Leveraging anatomical constraints with uncertainty for pneumothorax segmentation.Health care science · 2024Article
- Tiny Lungs, Big Challenges: Pediatric and Premature Lung Segmentation using Deep Learning.... International Symposium on Medical Information Processing and Analysis. International SIPAIM Workshop · 2024Article
- Patient Re-Identification Based on Deep Metric Learning in Trunk Computed Tomography Images Acquired from Devices from Different Vendors.Journal of imaging informatics in medicine · 2024Article
- Advanced Diagnostics of Respiratory Distress Syndrome in Premature Infants Treated with Surfactant and Budesonide through Computer-Assisted Chest X-ray Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- A deep learning-based cascade algorithm for pancreatic tumor segmentation.Frontiers in oncology · 2024Article
- Patient Identification Based on Deep Metric Learning for Preventing Human Errors in Follow-up X-Ray Examinations.Journal of digital imaging · 2023Article
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.Quantitative imaging in medicine and surgery · 2023Article
- Computer-assisted diagnosis for an early identification of lung cancer in chest X rays.Scientific reports · 2023Article
- OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique.Intelligent systems with applications · 2022Article
- Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance.
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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.