当前位置：
X-MOL 学术
›
IEEE/ACM Trans. Comput. Biol. Bioinform.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2020-07-20 , DOI: 10.1109/tcbb.2020.3009859 Yadunath Pathak , Piyush Kumar Shukla , K.V. Arya
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 3.6 ) Pub Date : 2020-07-20 , DOI: 10.1109/tcbb.2020.3009859 Yadunath Pathak , Piyush Kumar Shukla , K.V. Arya
In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems.
更新日期：2020-07-20
京公网安备 11010802027423号