ArticleJournal of healthcare engineering2018
Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04188938 (Occult Pneumothorax in Patients With Blunt or Penetrating Trauma), which is not on this map. Cited by 15 papers.
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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.
The trial behind it
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.
Occult Pneumothorax in Patients With Blunt or Penetrating Trauma: A Descriptive, Cross-sectional Study
Who cites it
15 citing papers in PubMed.
- An optimized transformer model for efficient detection of thoracic diseases in chest X-rays with multi-scale feature fusion.PloS one · 2025Article
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- Automatic and efficient pneumothorax segmentation from CT images using EFA-Net with feature alignment function.Scientific reports · 2023Article
- "Quo Vadis Diagnosis": Application of Informatics in Early Detection of Pneumothorax.Diagnostics (Basel, Switzerland) · 2023Article
- Medical image captioning via generative pretrained transformers.Scientific reports · 2023Article
- Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19.Materials today. Proceedings · 2023Article
- Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- A cognitive framework based on deep neural network for classification of coronavirus disease.Journal of ambient intelligence and humanized computing · 2022Article
- Deep learning based detection and analysis of COVID-19 on chest X-ray images.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- An Approach for Thoracic Syndrome Classification with Convolutional Neural Networks.Computational and mathematical methods in medicine · 2021Article
- Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study.BMC medical informatics and decision making · 2020Article
- Edge detection algorithm of cancer image based on deep learning.Bioengineered · 2020Article
- Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.European radiology · 2020Article
- Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.European radiology experimental · 2020Article
- Application of deep learning-based computer-aided detection system: detecting pneumothorax on chest radiograph after biopsy.European radiology · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical image segmentation is difficult due to the varying image quality among equipment. In this paper, the automatic method employed image multiscale intensity texture analysis and segmentation to solve this problem. In this paper, firstly, SVM is applied to identify common pneumothorax. Features are extracted from lung images with the LBP (local binary pattern). Then, classification of pneumothorax is determined by SVM. Secondly, the proposed automatic pneumothorax detection method is based on multiscale intensity texture segmentation by removing the background and noises in chest images for segmenting abnormal lung regions. The segmentation of abnormal regions is used for texture transformed from computing multiple overlapping blocks. The rib boundaries are identified with Sobel edge detection. Finally, in obtaining a complete disease region, the rib boundary is filled up and located between the abnormal regions.
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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.