Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

Irfan, Muhammad; Iftikhar, Muhammad Aksam; Yasin, Sana; Draz, Umar; Ali, Tariq; Hussain, Shafiq; Bukhari, Sarah; Alwadie, Abdullah; Rahman, Saifur; Głowacz, Adam; Althobiani, Faisal

doi:10.3390/ijerph18063056

Cited by 84 publications

(67 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection pipeline is a general framework and there are no strict restrictions. Hence, one could apply the same process to, for example, CT scan images [ 64 ], and even other areas such as those discussed in [ 65 ].…”

Section: Discussionmentioning

confidence: 99%

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Karbhari¹,

Basu

Geem

et al. 2021

Diagnostics

View full text Add to dashboard Cite

COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

show abstract

Section: Discussionmentioning

confidence: 99%

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Karbhari¹,

Basu

Geem

et al. 2021

Diagnostics

View full text Add to dashboard Cite

show abstract

“…where β 2 is a coefficient parameter to compute the EWA between v t−1 and g t . From Figure 8 and Equation ( 21), we can find that the long-term velocity control function is a momentum of β 2 1,t−1 (m t−1 − g t ) 2 . Accordingly, HyAdamC accumulates β 2 1,t−1 (m t−1 − g t ) 2 using the EWA to utilize a degree of these long-term average variations in the next steps.…”

Section: Long-term Velocity Control Functionmentioning

confidence: 95%

“…In particular, modern neural network models are consisted of deeper layers and more weights than traditional ones to maximize their performance. Accordingly, the latest deep learning models have shown notable abilities in many real-world applications, for example, computer visions (CV) [1,2], data analysis [3,4], personalized services [5,6], internet of things (IoT) [7,8], and natural language processing (NLP) [9,10], et al Among them, particularly, the CV task involving image classification and image semantic segmentation is one of the applications in which the deep learning models have been most actively used. Accordingly, many studies to improve the image processing ability of CNNs are being actively conducted.…”

Section: Introductionmentioning

confidence: 99%

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks

Kim

Choi

2021

Sensors

View full text Add to dashboard Cite

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.

show abstract

“…The pictorial data from chest X-ray images could be an alternative approach to the PCR screening technique. Many studies such as [ 20 , 21 ] depict the chest imaging of the body could help diagnose COVID-19. Radiologists have also found that patients with symptoms of COVID-19 have CT characteristic imaging features on their lungs, such as peripheral ground-glass opacities and consolidations, which can separate the patient infected with Coronavirus from those diseased with no Coronavirus [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Almalki

Qayyum²,

Irfan

et al. 2021

Healthcare

Self Cite

View full text Add to dashboard Cite

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

show abstract

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

Cited by 84 publications

References 40 publications

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Contact Info

Product

Resources

About