ArticleComputers & electrical engineering : an international journal2022
Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.
Article in Computers & electrical engineering : an international journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 27 citations in OpenAlex.
- Comparative analysis of multimodal architectures for effective skin lesion detection using clinical and image data.Frontiers in artificial intelligence · 2025Article
- Article
- Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications.PeerJ. Computer science · 2024Article
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- Midwifery learning and forecasting: Predicting content demand with user-generated logs.Artificial intelligence in medicine · 2023Article
- HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.BioMed research international · 2023Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.