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

Posted: 2021-11-04 19:00:00
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 recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 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.

参考サイト PubMed: covid-19


4月 21, 2020 バイオアソシエイツ


Gilead Sciences社は、世界の保健当局と緊密に協力して、調査用化合物「Remdesivir(レムデシビル)」(画像)の実験的使用を通じ、新型コロナウイルス( COVID-19 )に対応していることを報告した。米国食品医薬品局(FDA)、疾病対策センター(CDC)、保健福祉省(DHHS)、米国立アレルギー感染症研究所(NIAID)、国防総省(DoD)- CBRN Medical、中国CDCおよび国家医療製品管理局(NMPA)、世界保健機関(WHO)、そして個々の研究者と臨床医と共同して、Gilead…

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