Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients
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...
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Nature Portfolio
2021
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oai:doaj.org-article:9ceed63c3b1d4d089aecf42d5012cfba2021-12-02T16:23:42ZCombining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients10.1038/s41598-021-93543-82045-2322https://doaj.org/article/9ceed63c3b1d4d089aecf42d5012cfba2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93543-8https://doaj.org/toc/2045-2322Abstract 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.Fahime KhozeimehDanial SharifraziNavid Hoseini IzadiJavad Hassannataj JoloudariAfshin ShoeibiRoohallah AlizadehsaniJuan M. GorrizSadiq HussainZahra Alizadeh SaniHossein MoosaeiAbbas KhosraviSaeid NahavandiSheikh Mohammed Shariful IslamNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021) |
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Medicine R Science Q Fahime Khozeimeh Danial Sharifrazi Navid Hoseini Izadi Javad Hassannataj Joloudari Afshin Shoeibi Roohallah Alizadehsani Juan M. Gorriz Sadiq Hussain Zahra Alizadeh Sani Hossein Moosaei Abbas Khosravi Saeid Nahavandi Sheikh Mohammed Shariful Islam Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| description |
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. |
| format |
article |
| author |
Fahime Khozeimeh Danial Sharifrazi Navid Hoseini Izadi Javad Hassannataj Joloudari Afshin Shoeibi Roohallah Alizadehsani Juan M. Gorriz Sadiq Hussain Zahra Alizadeh Sani Hossein Moosaei Abbas Khosravi Saeid Nahavandi Sheikh Mohammed Shariful Islam |
| author_facet |
Fahime Khozeimeh Danial Sharifrazi Navid Hoseini Izadi Javad Hassannataj Joloudari Afshin Shoeibi Roohallah Alizadehsani Juan M. Gorriz Sadiq Hussain Zahra Alizadeh Sani Hossein Moosaei Abbas Khosravi Saeid Nahavandi Sheikh Mohammed Shariful Islam |
| author_sort |
Fahime Khozeimeh |
| title |
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| title_short |
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| title_full |
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| title_fullStr |
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| title_full_unstemmed |
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients |
| title_sort |
combining a convolutional neural network with autoencoders to predict the survival chance of covid-19 patients |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/9ceed63c3b1d4d089aecf42d5012cfba |
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