ArticleScientific reports2021
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.Cognitive neurodynamics · 2023Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- Body composition predicts hypertension using machine learning methods: a cohort study.Scientific reports · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- COVID-19 radiograph prognosis using a deep CResNeXt network.Multimedia tools and applications · 2023Article
- Hybrid feature engineering of medical data via variational autoencoders with triplet loss: a COVID-19 prognosis study.Scientific reports · 2023Article
- Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation.Computer methods and programs in biomedicine · 2023Article
- Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.Journal of digital imaging · 2023Article
- Article
- Prognosis prediction in traumatic brain injury patients using machine learning algorithms.Scientific reports · 2023Article
- Densely attention mechanism based network for COVID-19 detection in chest X-rays.Scientific reports · 2023Article
- CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.Frontiers in public health · 2023Article
- Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.Sensors (Basel, Switzerland) · 2022Article
- Generative adversarial network based data augmentation for CNN based detection of Covid-19.Scientific reports · 2022Article
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
- LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.International journal of imaging systems and technology · 2022Article
- Prediction and forecasting of worldwide corona virus (COVID-19) outbreak using time series and machine learning.Concurrency and computation : practice & experience · 2022Article
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
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