1. An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.
- Author
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Pustokhin, Denis A., Pustokhina, Irina V., Dinh, Phuoc Nguyen, Phan, Son Van, Nguyen, Gia Nhu, Joshi, Gyanendra Prasad, and K., Shankar
- Subjects
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COVID-19 testing , *FEATURE extraction , *DEEP learning , *COVID-19 pandemic , *MEDICAL screening - Abstract
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%. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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