1. Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.
- Author
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Kumar, Santosh, Gupta, Sachin Kumar, Kumar, Vinit, Kumar, Manoj, Chaube, Mithilesh Kumar, and Naik, Nenavath Srinivas
- Subjects
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DEEP learning , *EARLY diagnosis , *MULTIMODAL user interfaces , *COVID-19 , *CONVOLUTIONAL neural networks , *DIAGNOSTIC equipment , *MACHINE learning - 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%. [Display omitted] • A novel multimodal framework is proposed for the early diagnosis and accurate classification of COVID-19. • The extracted features are fused using the weighted sum-rule fusion technique for early diagnosis and accurate prediction. • The framework provided an accuracy of 98.67% (X-ray based) and 86.53% (cough based diagnosis) for early diagnosis of patients. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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