Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic

To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.

Section: Introductionmentioning

confidence: 99%

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection

Hou

Gao

2021

“…Currently, research on COVID-19 is being effectively carried out in various areas. Reference 20 review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases. Reference 21 discuss the different wearable monitoring devices and respiratory support systems which are frequently used to assist coronavirus affected people.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for COVID-19 detection based on CT images

Zhao

Jiang

Qiu

2021

138

COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.

“…Wearable technologies and how they can be used to provide initial treatment to people have also been investigated 37 . Ullah et al 38 reviewed telehealth services and the possible ways in which they can be used to provide patients with necessary treatments while keeping the social distance between patients and doctors.…”

Section: Literature Reviewmentioning

confidence: 99%

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

Khozeimeh

Sharifrazi

Izadi

et al. 2021

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.