Coronavirus Disease 2019 (COVID-19): Current Situation and Therapeutic Options

Singh, Vertika; Mishra, Varun Narayan

doi:10.2174/2666796701999201005211854

Cited by 20 publications

(1 citation statement)

References 75 publications

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“…Over the past few years, researches developed various computer-aided diagnosis systems (CAD) based on arti cial intelligence and machine learning algorithms to detect abnormalities in radiological images such as computed tomography (CT) and X-rays achieving promising results. CT and chest X-ray images (CXR) are generally considered to be fast and effective way for making clinical decisions 7 . Diagnosis of breast cancer 8 , epilepsy 9 , cardiovascular disease 10 , lung cancer 11 , and pneumonia 12 via deep learning models has become a popular technique in the medical eld.…”

Section: Introductionmentioning

confidence: 99%

EpistoNet: An ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images

Mojab

Shams

Fotouhi

et al. 2021

Preprint

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The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a novel hyper-heuristic evolutionary method. Using 2,500 chest X-ray images consisting of 1,250 COVID-19 and 1,250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2,000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.

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