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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

In-depth understanding of LSTM and its recent advances in lung disease diagnosis

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  • In-depth understanding of LSTM and its recent advances in lung disease diagnosis

Abhinandan Kalita *

Department of Electronics and Communication Engineering, Girijananda Chowdhury Institute of Management and Technology-Guwahati, Assam, India.
 
Research Article
World Journal of Advanced Research and Reviews, 2022, 14(03), 517-522
Article DOI: 10.30574/wjarr.2022.14.3.0602
DOI url: https://doi.org/10.30574/wjarr.2022.14.3.0602
 
Received on 16 May 2022; revised on 19 June 2022; accepted on 21 June 2022
 
Of late, long short-term memory (LSTM) has proven its worth in medical diagnosis. Hence, there is a need to explore this special version of recurrent neural network (RNN), which can learn long-term dependencies. LSTM addresses the short-term memory problem of basic RNNs. In this paper, an in-depth study of LSTM is done with the help of a few real-life examples. Some of the recent advances of LSTM in COVID-19 and other lung disease diagnoses have also been discussed. 
 
Recurrent neural network; Vanishing gradient; Long short-term memory; Deep learning; COVID-19
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2022-0602.pdf

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Abhinandan Kalita. In-depth understanding of LSTM and its recent advances in lung disease diagnosis. World Journal of Advanced Research and Reviews, 2022, 14(3), 517-522. Article DOI: https://doi.org/10.30574/wjarr.2022.14.3.0602

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