ArticleBiomedical signal processing and control2023
D
Article in Biomedical signal processing and control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Gated Backbone Fusion with Transformer Encoder for Diabetic Foot Osteomyelitis Screening and Localization in Radiographs.Journal of imaging informatics in medicine · 2026Article
- Computer-based quantitative image texture analysis using multi-collinearity diagnosis in chest X-ray images.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covid-19 is one of the biggest global epidemics seen in the world in recent years. Because of this, people's daily lifestyles, the economic conditions of countries and individuals, and most importantly, their health status has been adversely affected all over the world. Millions of people around the world have died from this disease. For this reason, rapid and accurate detection of the disease is of great importance in terms of treatment and precautions. In addition, it is especially important to correctly distinguish between Covid-19 and non-Covid-19 pneumonia diseases for correct diagnosis and treatment. These two diseases cause similar symptoms, and the symptoms and the effects of the disease on the body should be carefully examined for their differentiation. Chest X-ray images, chest computerized tomography, and swab tests are commonly used to detect patients infected with COVID-19. This disease affects the lungs the most in the body and causes fatal side effects such as shortness of breath. Therefore, medical images taken from the chest play an important role in the diagnosis of the disease. The fact that X-rays are faster and cheaper than computerized tomography has led to an increase in studies on the detection of disease with X-rays. In recent years, the impressive results of deep learning in the field of computer vision have attracted researchers to this field when working with image data. This study aims to detect these diseases on chest X-ray images collected from Covid-19 patients, pneumonia patients, and healthy individuals. We proposed a hybrid feature extraction network namely D
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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.