ArticleJournal of medical systems2021
Covid-19 Imaging Tools: How Big Data is Big?
Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.International journal of medical sciences · 2022Pooled it
- Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system.Scientific reports · 2024Article
- Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation.Heliyon · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives.Biomedical signal processing and control · 2023Review
- SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.International journal of machine learning and cybernetics · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.Healthcare (Basel, Switzerland) · 2023Review
- Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS).Frontiers in physiology · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays.Expert systems with applications · 2022Article
- Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images.Scientific reports · 2022Article
- TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images.Scientific reports · 2022Article
- Anatomical variants identified on chest computed tomography of 1000+ COVID-19 patients from an open-access dataset.Clinical anatomy (New York, N.Y.) · 2022Article
- Neural architecture search for pneumonia diagnosis from chest X-rays.Scientific reports · 2022Article
- Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.Information sciences · 2022Article
- Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.Journal of medical systems · 2022Article
- COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest X-ray Images.Frontiers in medicine · 2022Article
- Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.Frontiers in physiology · 2022Article
- The value of chest X-ray and CT severity scoring systems in the diagnosis of COVID-19: A review.Frontiers in medicine · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.
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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.