Статья

An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19

D. Pustokhin, I. Pustokhina, P. Dinh, S. Phan, G. Nguyen, G. Joshi, K. Shankar,
2020

In recent days, COVID-19 pandemic has affected several people's lives globally and necessitates a massive number of screening tests to detect the existence of the coronavirus. At the same time, the rise of deep learning (DL) concepts helps to effectively develop a COVID-19 diagnosis model to attain maximum detection rate with minimum computation time. This paper presents a new Residual Network (ResNet) based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM for COVID-19 Diagnosis. The proposed RCAL-BiLSTM model involves a series of processes namely bilateral filtering (BF) based preprocessing, RCAL-BiLSTM based feature extraction, and softmax (SM) based classification. Once the BF technique produces the preprocessed image, RCAL-BiLSTM based feature extraction process takes place using three modules, namely ResNet based feature extraction, CAL, and Bi-LSTM modules. Finally, the SM layer is applied to categorize the feature vectors into corresponding feature maps. The experimental validation of the presented RCAL-BiLSTM model is tested against Chest-X-Ray dataset and the results are determined under several aspects. The experimental outcome pointed out the superior nature of the RCAL-BiLSTM model by attaining maximum sensitivity of 93.28%, specificity of 94.61%, precision of 94.90%, accuracy of 94.88%, F-score of 93.10% and kappa value of 91.40%.

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Версии

  • 1. Version of Record от 2020-01-01

Метаданные

Об авторах
  • D. Pustokhin
    State University of Management, Moscow
  • I. Pustokhina
    Plekhanov Russian University of Economics
  • P. Dinh
    University of Natural Sciences Vietnam
  • S. Phan
    Duy Tan University, Duy Tan University
  • G. Nguyen
    Duy Tan University, Duy Tan University
  • G. Joshi
    Sejong University
  • K. Shankar
    Alagappa University
Название журнала
  • Journal of Applied Statistics
Номер гранта
  • undefined
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • scopus