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 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.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

“…Then, these 2 D images are applied to different 2D deep learning networks. Another future work is using novel DL techniques such as attention learning [119][120][121][122], transformers [123,124], and other advanced deep learning techniques [125][126][127][128][129][130][131][132][133][134] for epileptic seizure detection. Finally, adopting novel deep feature fusion techniques to epileptic seizures detection based on EEG signals can be noteworthy as one of the future works [135].…”

An effective detection of COVID‐19 using adaptive dual‐stage horse herd bidirectional long short‐term memory framework

“…The accuracy of classification obtained with the CT images was 96.71%, whereas the existing classifiers DT‐GFE (84.57%) and RF‐GFE (85.62%) gained lower results. Khozeimeh et al 41 developed a model for predicting COVID‐19 in CT images combining CNN‐autoencoders (CNN‐AE). The chance of survival was analyzed accurately with CNN‐AE considering the clinical features.…”

Epidemiological challenges in pandemic coronavirus disease (COVID‐19): Role of artificial intelligence

“…More recently, a similar type of investigation has been presented by Khozeimeh et al (2021). Here, the authors have predicted the chance of survival of COVID-19 patients based on clinical information.…”