ArticleJournal of applied statistics2023
An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM.PloS one · 2025Article
- Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.Journal of imaging informatics in medicine · 2024Article
- ERABiLNet: enhanced residual attention with bidirectional long short-term memory.Scientific reports · 2024Article
- Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.Scientific reports · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model.Cognitive neurodynamics · 2023Article
- Editorial to the special issue: Statistical Approaches for Big Data and Machine Learning.Journal of applied statistics · 2023Article
- Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity.Diagnostics (Basel, Switzerland) · 2022Article
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- Weather Conditions and COVID-19 Cases: Insights from the GCC Countries.Intelligent systems with applications · 2022Article
- An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification.Healthcare (Basel, Switzerland) · 2022Article
- Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images.Multimedia systems · 2022Article
- Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification.Pattern recognition letters · 2021Article
- Fundus image classification using feature concatenation for early diagnosis of retinal disease.Digital healthArticle
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Authors and funding
7 authors.
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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%.
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